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Recommendation Systems - Science topic

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So Basically, I want to create a query recommendation system which recommends to the user a set of queries, the answer set of which contains data that are of interest to the user. A query that is recommended for a user is a query that the specific user has not posed to the database in the past and is expected that if posed, the user will give a high score. Note that although the query may have not been posed by the specific user in the past, some of its tuples may have in the past been seen by the user, maybe because they were in the answer set of another query that the user has posed.
Any suggestions would be highly appreciated.
Thank you so much.
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Assuming that the user already put in a couple of query words, you could in a first step tryo to find out in what relationships these words are. For example, if the words are antonyms, you could suggest "what is the difference between * and *". You will need something like an ontology.
Regards,
Joachim
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When it comes to recommendation systems and popularity based item recommendation, what is the most popular algorithm ?
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fair enough Mourad Raif
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What are the different ways to get user embeddings apart from matrix factorization of user-item matrix ?
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Thanks Babatounde Moctard Oloulade for the direction. But the main challenge lies in how we are defining the graph from both user and item point of view.
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Hello dear all.
I would to build a recommendation system to suggest questions and seek for right resources to read and suggestions. The idea is to build a recommender system like for stack overflow that will suggest to a user when he is typing his question similar or match questions that are already answered by the forum in order to avoid asking the same question. This will help people answering new questions rather than giving an answer to the same question again ( as people have many issues everyday and use developers forum for help).
Thanks
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1. How much data do I need?
2. We have this system in place, how do we know whether it is sane?
3. Which algorithm is best?
4.How efficient is the system?
5.What is the temporal and computational complexity of each subsection?
etc
Please consider these links
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Is there a dataset that contains
  1. user-item ratings and metadata about items such as names (like MovieLens)
  2. user-user explicit trust relationships (like FilmTrust)?
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Ali Fallahi One of the most often used recommendation algorithms is collaborative filtering (CF) and its variants. Even inexperienced data scientists may use it to create their own personal movie recommender system, for example, for a CV project.
Recommender systems are used by Netflix, YouTube, Tinder, and Amazon. The systems attract users by making relevant suggestions depending on their decisions. Recommender systems may also improve the user experience of news websites.
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Have your manuscript been transferred by article transfer service of Elsevier to another journal?
How do you find it?
Was it helpful? why?
If you are not familiar with the service, please check the link below:
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I agree with Michael Frömmel that it is good to have this recommendation at the start of your contact with a new journal but I agree with Subrata Chakraborty and
Abdelrahman I. Abushouk
that you have to be very careful because some of those journals have ridiculous or exorbitant APC requirements.
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I am doing a graduation project about a recommendation system that recommend your outfit from the clothes you have in your wardrobe but I am struggling on what is the best way to create a system like that And i Would like your help.
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I would first sit down and describe at a high level the features that are desired for each of the modules. Be sure to account for feature dependency between modules. After doing so, the stage for particular algorithm that targets the different features can be derived. You can contact me here or privately to continue the discussion.
Regards
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I am now very interested in applying causal inference to the design of recommendation system algorithms. However, there are no good ideas yet, so if there are researchers who have a common interest in this area, we can discuss it together.
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Hi I am working with Simpsons Paradox and XAI. You explore it and if it is interesting for you, we can collaborate.
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All,
I need your suggestions/ideas on M.Tech dissertation topics in DataScience in NMS/EMS preferably in Telecommunication domain. Thanks in advance.
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If you are interested in assessing the performance of modelling approaches, please let me recommend these works:
If your topic assumes the comparison of alternative approaches, the FEW-L1 workflow presented in the article above can be used.
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When I am answering a question or adding to the posts in a discussion, I like to mention the name of a researcher and use the @ in order to bring up an active link. I have just been trying to mention the name of a RG member who has been very highly recommended for the answer supplied, but can't make RG recognise the person's name. The highly recommended response was made in January 2021, but I would have thought that anyone who contributed to that particular thread should have come up as being recognised. But no! Not even when the response has been so highly recommended!
I realise that once you have added a response, that this method does not function, and I usually copy my response, delete the box of what I have just added and paste it into a new response box. Then I can use the @ for an active link - - - unless the RG member responded too long ago.
What are your views on this?
Sometimes, a responder uses the @ before a RG member's name, so it looks as if the response had not appeared as was hoped.
I found it very difficult to find words describing this question that RG would accept and recognise as this was not a question about a recognised subject.
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Hi,
I have not notices it until now. That is a bit strange way to write a link.
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Filling out input forms for (mobile) data acquisition is often tedious and, moreover, prone to errors. And it can often be observed that input sequences are repeated so that, in principle, workloads could be avoided. An innovative approach to overcoming these problems would be a situation-dependent "intelligent" prefilling of the data entry forms. (Situational factors would be, e.g., acquisitions already made by the user in the past, the current geographic location, time of day, etc.) In other words: On the basis of the forms already filled out in the past, the idea is to have a "recommendation system" that suggests suitable candidates. At best, the system should "learn" from the acquisition processes that have already been completed.
My question: Who knows work that has already dealt with "recommendation systems" specifically for input forms? (Here, input forms consist of a possibly large number of data fields that have to be filled with values.)
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Thanks for the answer, Hardy! Yes, please ask your colleague! The topic of generating recommendations for filling out data entry forms is interesting! Best regards, Benno
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Hi,
I am doing a research about real-world implementations for Educational Resources Recommendation System (ML/DL) within the eLearning domain. For example in a university LMS.
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The recommendation system is a very trending topic at present. It is basically an extension of semantic analysis.
You can follow these articles for better insight.
Hope it will help you.
With regards,
Sayan Surya Shaw.
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  1. What are the hot topics and future topic for the Recommendation System?
  2. Can we publish High Impact Factor Papers on Recommendation System?
  3. Any other topic suggestions for PhD Computer Science that benefit the student in long term?
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Regarding your question,
1. Recommender system is one of the hottest topics recently and good quality work in recommender system has a good opportunity to be published in high impact journals.
2. There are many hot topics in RS, such as using the user reviews to solve the two most famous problems of RS: data sparisty and cold start problem, you can check this recent survey and at the end of it there are some future trends,
Also, you can use deep learning techniques to enhance the recommender system accuracy/performance.
Good Luck
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i want to built a tour recommender system for group of people but there is only individual users dataset is available. anybody have any idea how we can convert individual data into group
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Use k- mean to cluster the people into groups. Set the number of groubs k to equal the number of plans you have. Finally, use these groups to build your system
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The current research is more inclined towards the preference of deep learning models. In this context, I want to know about the various Deep nets which will be useful for designing recommendation systems.
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1) If data is in the form of healthcare images (e.g. MRI results, X-rays, CT Scan Images etc.) then CNN is the best choice. See the following recent top-level experimental evidences:
i. According to a study published in June 2018 in “Annals of Oncology” [1] , a research work was carried out by a team of experts from German institutions. The goal was early detection of Melanoma (the most serious type of skin cancer) through dermoscopic images. They trained and tested a deep learning CNN for differentiating dermoscopic images of melanoma and benign marks. The team set out to compare its diagnostic performance to that of 58 dermatologists of various levels of expertise. The convolutional neural network (CNN) scored 10% higher in terms of specificity than human experts.
ii. According to an article of August 2018 [2], researchers at the Mount Sinai Icahn School of Medicine (USA) trained CNN for early detection of acute neurological events (like stroke, hemorrhage and hydrocephalus etc.). They used 37,236 CT scan images for its training and their algorithm detected disease in CT scan images in just 1.2 seconds that was 150 times faster than human radiologists.
Also, there are many more experimental studies in this context and you can see them if you want to work using deep learning in such healthcare data applications.
2) If the data is in the form of text (like medical reports or any other health care documentation) then those deep learning models will be more suitable that had already given good results in NLP (natural language processing) applications because such algorithms can understand the complex semantic meaning in the text. Such algorithms are like LSTM (which is in fact the special type of RNN) as these algorithms are capable of learning long-term dependencies and good in sequence prediction problems. Hence they are well suited for classifying, processing and making predictions regarding text analytics which demands understanding textual data based on the sentential context.
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Hi every body
I need to collect datasets from different source to work on recommendation system algorithms, could you advise me how collect data?
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very useful, thanks
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My project is related to these questions and I want to inquire about these two terms to know if can use them in the recommendation system
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At its simplest, market segmentation is the process of dividing a market into segments and directing a marketing mix to a particular segment, rather than the total market. In line with your question, product segmentation has special relevance for the retail sector, where the range of the offering (products + services) is very wide. For a startup it is important to start with market segmentation or market niche.
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Has it occurred to you that you want to watch movie and Netflix is providing the same set of movies based on your previous history? what if we are in a different mood or different mindset and want to watch something different, how good these systems are in those situations. May be thses are not good for those who want to watch diverse range of movies.
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Dear Sandhya,
Many models are indeed biased and you often get the same set recommendations over and over again due to your browse history in those systems. For example, consider that from September till May you watch horror movies, while from April till September, you watch comedies. Obviously, there is a change in your preferences but most models can't understand this evolution. Only a few research has been conducted in the literature related to this phenomenon that is known as "preference/temporal dynamics". It seems that in most online services this feature is not supported yet.
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Whar are main difference between IOT and big data in recommendation system?
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kindly you can click the link below to find the answer:
Or:
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I search for applications about recommender systems.
The Idea in general is to use algorithms which gives according to historical data of the classifications of soil. then make a mapping spell between the existing data of soil, weather and crops and an input entered, user to know the type of crop adapted to his soil in addition to that the amount of appropriate fertilizer.
If you have research/review articles comparative studies or support it will be very helpful .
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We still need basic data , which will come from crop-nutrient repsonse studies in field or the basic dataset in field need to be subjected to machine learning duly cross validated in order to set right the suitability for practical application in filed...
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To put you in the context, our work consists in realizing a machine learning model which takes a vector with the properties of a farm, includes the weather why not.Then from a database of crops, make a recommendation of the most suitable crop for the soil. Therefore a recognition on the elements which help in this decision is an important part before starting the collection of the data necessary for the model.
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I would like to recommend you to go through two of my articles:
1. B.K.Tripathy and Sooraj, T. R.: An Interval Valued Fuzzy Soft set Based Optimization Algorithm for High Yielding Seed Selection, International Journal of Fuzzy Sets and Applications, IGI publications, vol.7, issue 2, (2018), pp. 44 - 61.
2. B.K.Tripathy, Sooraj, T. R.: Optimization of seed selection for higher product using Interval valued Hesitant Fuzzy Soft Sets, Songklanakarin Journal of Science and Technology (SJST), 40 (5), Sep. -Oct. 2018, (2018), pp.1125-1135 .
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The question surrounds about applications where this two concept exists. if you have comparative studies, review articles or support it will be very helpful.
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Hicham Chouikh Fertilizer will be recommended using nutrient status table stored in the database. By comparing values of nutrients with table classification will be done. And accordingly fertilizer will be recommended to the user. Our system will help farmers for better crop yield which in turn maximizes profit. I suggest you follow: http://www.informaticsjournals.com/index.php/mvpjes/article/download/18273/17610
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I search for applications about recommender systems where there is Similarity techniques in the field of agriculture .
Idea in general it is to use algorithms which gives according to historical data of the classifications of soil. then make a mapping spell between the existing data of soil, weather and crops and an input entered, user to know the type of crop adapted to his soil in addition to that the amount of appropriate fertilizer. my question targets similarity systems and their uses in these applications.
If you have articles or links on this topic it will be very helpful.
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Most common techniques used for signature matrix are jaccard similarity and cosine similarity. I would prefer to use cosine similarity for given circumstances.
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Has anyone used one of these journal recommendation systems? How helpfull are they? how accurate are they?
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I think springer and elsevier are the best publisher to find the suitable journal .
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Please suggest some research directions in Recommended Systems
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The field of recommender systems, especially in academia, has been focusing mainly on the accuracy-related measures of successful recommendation. Many people have worked on things like how to improve accuracy of the recommendations by a tiny bit while the real effect of this improvement is really not clear. However, the real-world industrial recommender systems have proved that this over-emphasis on accuracy is wrong and there are other measures that have a better correlation with the success of a business. Things like diversity, novelty, long-tail recommendations, and removing biases. In addition, the new notion of multi-stakeholder recommendation has opened a lot of doors for further research. For example, taking into account different stakeholders' needs of a given recommender system is critical for the long-term success of the business. Think about Spotify, for instance. Spotify's success depends on the satisfaction of both the listeners and the artists. Therefore focusing only on the listeners may not necessarily lead to long-term success. To learn about the research directions in Multistakeholder recommendation I suggest our paper at UMUAI journal which can be a great read for someone who is looking for novel ideas.
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Hey. I'm new here, so please let me know if it is not an appropriate question.
I think about taking advantage of the Markov Decision Process (MDP) in the recommendation system. I know that it's possible as described here: https://medium.com/inside-machine-learning/recommendation-systems-using-reinforcement-learning-de6379eecfde but have no more details regarding it.
I would like to implement this system in my startup. I'm the CEO of a language learning platform Linguido where we leverage the power of storytelling and let users learn languages by reading engaging interactive stories in combination with several language learning tools. We know that everyone enjoys different stories, so we would like to implement a system that would match the right content with the right user based on their interests and known vocabulary.
However, to successfully deploy such a thing we have to ask for funding. If you happen to know anything about the cost of such a project or time necessary to develop it I will be grateful if you share with me this information. Best regards
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The start may be a bit challenging, however., if you really need to implement it and keep using in your tasks it will be a fun, see some sources e.g., https://github.com/awarebayes/RecNN
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Recommendation Engines algorithms are typically based on collaborative and content based filtering methods and / or combination of both. In our project we are working on Next Best Action and Offer with Dynamic Recommendation Systems. Details can be found at:
However we would like to hear suggestions and alternative recommendations from the researchers who are working on recommendation engines (aka recommender systems). What are the software platforms, software packages, approaches, algorithms and tools do you recommend for Recommendation Engines? How do you relate your solution to big data analysis, machine learning or deep learning frameworks?
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At PUC Chile we developed a python software package, pyreclab, to quickly prototype and develop recommendation systems. It might help you to move forward using Colab notebooks to test traditional models:
If you have further questions let me know.
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Usually, the publishing houses like Elsevier and Springer have their own recommendation system to suggest the best journal to submit the paper for a given topic and abstract. But is there any 3rd party tool that can generate recommendations across all journals ?
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There are third pary journal recommendation systems like JANE (Journal/ Author Name Estimator), Edanz, C&K recommender (which was developed for a reaserch degree), Manuscript Matcher, and so on. These systems vary from one to another based on the similarity algorithms used, subject domains, journal databases used, recommendation methods (i.e. content-based, knowledge-based, hybrid, etc.) employed, and so on. You can find more systems and a comprehensive comparison among them from the following link:
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Hi all,
I do realize that this question title might sound confusing. I want to publish my research article into a machine learning journal/ conference that has a good amount of previous articles on movie recommendation engines.
Thanks in advance
Roy
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I don't want a recommendation system.
I want to group similar users who select similar items .
I want to then cluster them and label those clusters.
Jaccard similarity is not efficient because I have millions of users and items.
Matrix factorization gives the recommendation model. I don't want to recommend any items. I just want to group them and label.
I don't know if k-means clustering works well for multiple feature vectors.
Does anybody know to implement this?
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I would recommend to also look into Boolean matrix factorization (BMF). k-means/modes is only usable if clusters have convex shapes and every point belongs to exactly one cluster. BMF can compute clusters with overlap (might not be necessarily important for your application) but it also identifies the features which are important for creating one cluster together with the data points belonging to one cluster. Especially if you have a high-dimensional feature space, it is unlikely that the similarity between points is expressed over the whole feature space. BMF identifies the feature space in which the points form a cluster.
If you are interested, look at Chapter 2 (particularly Section 2.4) from
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We have a recommendation system based on emotion recognition and we have doubts about which algorithm we have to use. according to our currency search, we have found some classifiers such as ROI for detecting facial areas and 3NN MLP for emotion classify process.
Since we are new in this ML we really need help and advice for this topic.
Thank you.
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There is a possibility that you develop your own algorithm. What do you need for it?
* sufficiently large database of annotated facial expressions
* when you have none, you can build your own from a video recording of people changing facial expressions
* you will extract features, positions of special points at the face -- this you know how to do better than me
* you use AI and ML techniques to distill changes in features that correspond with given emotional states
* you can go for static evaluation of features
* or you can go to dynamic evaluation
It all sounds like a really interesting project. What I can recommend to you is to learn how biosignals are processed using complex systems and AI & ML techniques. It can give you a big kick in hoe to proceed in your case.
All the best :-)
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Hi,
i want some work that speak about social network based recommendation system or that exploit the profile of a user
thanks
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A determinant of organizational performance is the ability to leverage expert knowledge: much of that knowledge is tacit and therefore difficult to capture, codify, and make available through search engines and database technologies. And so, when looking we usually turn to people we know for quick, reliable information (and chance conversations can help too.) However, in the connected economy, personal networks are no longer sufficiently diverse to identify all the right persons, much as reliance on random connections is a thing of the past. Staff directories are no longer adequate to the task: therefore, learning organizations thrive on rich and fluid linkages and use expertise location capabilities to put people in contact with one another.
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I am thinking of creating a search engine to help people find a movie or similar movie based on the snippets of the story. For instance, if a user type in "movie about dog waiting a long time for his owners to come back", the result should return "Hachiko" , "Eight below", "Lassie" etc. However, it would have been better if we can use data mining method to actually search it based on the plot of the movie not keywords. ​What is the best solution for this work?
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@Raju_Balakrishnan3
Thank you so much.
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I proposed a comprehensive recommender system for e-commerce usage, but unfortunately i can't find any data-set for evaluation step. I need a data-set containing:
1- Categories
2- Product features (category, price, color, brand, author, RAM and etc. that can be diverse according to the category)
3- User demographic information (age, gender and etc.)
4- User purchase history
5- User browsing history (visiting product's page)
Can anybody help me to find a data-set with this features please?
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I would like to know which method SVD or KNN will yield better prediction accuracy in recommendation systems. Has anyone done a comparative study which I can refer to.
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The accuracy depends on the source of the data and the target objetive.
Most of cases, kNN is better in sets of data with low missing date proportion. And SVD is better in huge size data sets.
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Especially in discussions, I've had a tendency to use the recommendation system as a "like" button. However, I wonder if this is a good idea or if we should limit its use to cases where a comment provides detailed information relating to a question, rather than just something that we might like or that might interest us. Thoughts?
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Daniel,
You have asked a very important question that I have been asking myself in terms of the perceived goals of RG. My take is that RG is not a site to post trivial questions or answers. Questions and answers should follow some rigor in formulation. In my view it is a site to post information to clarify and further the areas of knowledge.
Some people take to RG as if this was Facebook and I think it trivializes the purpose of the site.
Regards
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I am working on a intention oriented recommendation system, however I don't want to crawl web to get user intentions. Is there any technique to synthetically generate user intentions to prove the concept?
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Pavlos Kefalas Dear, I using MovieLens Data for experimentation in this regard, I cannot use the time taken to read the review, can you suggest any other additional information that can be incorporated that can be used as intention?
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has been studying this paper " defining and supporting narrataive driven recommendation" by Bogers and Koolen. Should I continue? I am still an undergrade student.
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Hello,
I see that Marijn Koolen is a ResearchGate member; might it be worth asking him for his advice?
Very best wishes,
Mary
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looking for best way to implement a Recommendations Without User Preferences. Content-based recommendation as one of content similarity measurement. Let say we have all of article the user can read from , we need to provide each user with a list of top 5 most similar article that are similar to that article . please help with any simple guide , implementation example, case study, any help is appreciated .
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  • You can you contents of an article to extract topics/keywords or frequent words.
  • Use tfidf vectorizer to create a model (remember to remove stopwords).
  • Then use cosine similarity to get similar articles.
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i am new in research and masters student i need some help regarding to my topic collaborative filtering item base recommendation system please help me
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Hi Tareq,
my suggestion is to read the chapter about recommender systems in "Mining Massive Datasets" http://www.mmds.org/. There you can get the idea for which types of problems the two basic approaches are good for and that often you need an hybrid approach.
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Hi
I am researching in the field of trust-aware collaborative filtering systems. I have developed a movie recommender system which is based on an implicit trust network. The implicit trust between two users is calculated based on their co-rated items. Recently, a reviewer has said that "social networks provide a lot of structural information. You only considered the adjacency, which is the most basic feature."
I want to know what are the other features of social network structure that can be taken into account? and how these features can be applied to a recommender system which works only based on rating data?
Thank you in advance.
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Adjacency as the reviewer pointed is the most key factor and relationship
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There is a set of data from an emission measurement sensor sorted by the time they were measured.
Now I need to know how I can find the time pattern when a sensor goes out of calibration?
I want to use a real time model for calibration of emission measurement sensors in automobiles.
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You can conduct time-series analyses with ARIMA-GARCH models and use residuals or standardized residuals to examine any outlier effect. Also, you can examine change/break-points in these residual series with several statistical techniques. A proper ARIMA-GARCH model should provide you normally distributed residuals with no serial correlation.
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There are some products have different names but same meaning. How to tackle Label Synonym and Label Polysemy problem in Recommendation System.
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Suppose there are three synonyms i1, i2, and i3 of the same product. The simplest solution is to use dictionary to merge them as item i4. However, if user u1 rates on i1 and i2 by values v1 and v2 then, rating value of u1 on i4 is the average value (v1 + v2) / 2.
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how to combined recommendation system with big data and optimization techniques?
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Hello,
Recommender Systems could be applied for many large-scale e-applications like e-commerce, e-learning, etc.
In fact, Recommender systems are being more powerful
with the huge volume of customer data in existing corporate
databases (structured Big Data), and the
increasing volume of customer data available on the
Web and social media platforms (unstructured Big Data).
You can check these links for more details:
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I have built course recommendation system I would like to know how can make an evolution for the framework? Will be enough to use a questioners and users satisfaction as factors for evaluation in the course domain ?
Kind regards
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Dear Ibrahim,
Comparison is always acceptable in view of justifying your claim. If it can be shown, it is well taken.
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Hello All,
I am predicting recommendation movies.
I am passing the command line argument as userId.
Rscript example.R userId
"Rscript example.R 80"
Save and load model working perfectly.So i am facing the issue in predict in Movies.
I upload the error and code images Please review it and give me suggestion.
Thanks in Advance.
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In my dataset
ratings.csv
userId, movieid, ratings
1 123 4
1 456 5
1 231 1
movies.csv
movieid, moviename, genre
123 Titanic love|romance
456 Avatar Scifi
231 Diehard Action
Now i have to recommend which i give top rated movies and which genre i used mostly that should be.
so result should be like movie id 456
I am using R Programming.
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I think I can understand the problem.
Generally, in personalized movie recommendation system, we use either content-based collaborative filtering or item-to-item collaborative filtering, or both in hybrid two-layered recommendation engine. In brief, the content-based collaborative filtering provides recommendation based on the genre of that particular movie/es you watched or rated. That means, if you initially watched horror movie/es, you will continually be recommended the horror movies. On the other hand, item-to-item collaborative filtering provides recommendation based on the similarity of your movie watching pattern with the other users' watching/rating pattern.
I think the following link can help you with the necessary coding.
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i'm working on big data project for a telecom company where i have to implement a recommendation to insure the Customer Experience Management (CEM) and improving the Quality of user Experience (QoE), so i need your help, suggest me recommendation use cases that i can implement in order to insure a good level of QoE, thanks
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thanks Nikola S. Nikolov i will read it and give you my feedback, thanks
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In Item-to-Item CF, the similarity is computed using the ratings assigned to items bought by users.
What if I have a data for an offline retail or restaurant where the items have no ratings since they are not sold online?
What other criteria that I can use or the way I should follow to extract a measurement that will work like the rating thing?
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You can group them in terms of similarity in use or functions for the purpose of comparison.
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I have two matrices, user-rating matrix and item metadata matrix, the item metadata matrix was clustered while the user-rating matrix was not clustered. is it possible to integrate the two together for collaborative filtering (CF) recommendations? If so, how?
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Thanks Nidhi for your answer.
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I have to learn the social recommendation system just now. 
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could you send to me the dataset, because of it is for a data mining assignment. If you still keep the related dataset. thanks a lot!
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I am looking for a reliable and effective live cell imaging system to measure long term GFP expression in transfected primary human neurones. I would like to measure the GFP signal from these cells over a period of days-weeks.
Does anybody have any good recommendations for systems that have worked well for them?
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The Nikon Ti-E epifluorescence setup is very flexible, you can very easily programme different time courses, and it can reliably maintain focus and xy-position over extensive time periods. We have used it for continuous neuronal live cell imaging for up to five days.
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How to choose the sub-set, and how to evaluate
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To evaluate a recommender system, you require some ground truth. Split your dataset into say 80% training set and 20 % test set. Use the 20% for test. Often times, people design a confusion matrix and calculates metrics such as precision, recall, F-score and plot ROC. study each of those to know which is suitable to you.
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I have actually proposed an approach to recommend experts for a newly posted question in a Community Question & Answer site, in where at the end a list of ranked Top-n experts will be recommended to the question owner. I am planning to evaluate the recommendation list with the actual users who have answered the similar questions and has received high upvotes from the original dataset. Is precision and recall a good measure to evaluate the recommended list of users or is there any better approach?
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Thanks Pavlos and Carla !
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I am looking for news datasets with longitude and latitude information for personalized news recommender systems. Could anyone suggest data sources?
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Dear Pengtao,
Another dataset match the description can be found in the following link:
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Thanks..
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It depends on the recommender system that you needs but  you can visits  eBOOK at boonbook for more informations
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Hi,
I am looking for an open source recommendation system based on content filtering to personalise suggestions for the user. My project needs data analysing from the health symptoms. I am using weka as a machine learning tools to analyse data. I need a recommendation system to use that data and personalise data using filtering and determine a solutions.
I am really stuck how to progress further. :/
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I suggest you take a look at MyMediaLite (http://mymedialite.net/). It's a library that contains a set of recommender algorithms (including UserKNN and ItemKNN).
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Hi dears
I have a dataset of some objects, e.g. pictures or texts, then I extract some features from those objects and put them in another dataset with a label that could be id or name of those objects. Now I want to identify similar objects to a new object in test environment, for example when a new picture have been taken and feature extraction is done, now I want to know which objects in train dataset are similar to this new object and get a list of top 10 more similar objects. Which machine learning tools or algorithm can help me to do this?
I know that I can calculate similarity of new object to another objects by many formula such as Eucleadus distance or other distance formulas, but when train dataset objects have a lot of objects, it will take long time to do this approach, I need a quick tools to do this. I have some ideas for this purpose but I want to know, is there any available approach now?
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from the question it appears that you know the solution. but you are asking that it is taking lot of time for computation. 
my answer is -
yes it takes time because you are trying to segregate the objects and creating lots of datasets. it is obvious. 
any machine learning tool is OK. but the latest ones that i experienced are 
SVM. 
your idea has been implemented in the following paper. 
here in the paper i have taken a face and divided the face in to different parts. but i used the same SVM tool as i used before dividing the face. 
you may get an idea if you look at it. 
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Hi everyone,
Is there a pertinents work on applying LOD (Linked Open Data), or ontologies, in a collaborative filtering.
Or, in other termes, how to introduce semantic data in a collaborative filtering.
Thanks,
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Hi Mohammed,
I can refer following works that combines linked open data and recommendation systems.
Start by reading Tommaso Di Noia papers; he has an important work on the subject. Here papers that you can start with:
* Recommender Systems Meet Linked Open Data.
* Recommender Systems and Linked Open Data.
* Content-Based Recommendations via DBpedia and Freebase: A Case Study in the Music Domain. 
Take a look on his papers here:
You can also take a look on these papers:
[1] R. Meymandpour and J. G. Davis, “Enhancing Recommender Systems Using Linked Open Data-Based Semantic Analysis of Items,”
[2] A. Passant, “dbrec - Music Recommendations Using DBpedia,”
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Suppose you have a recommender system, based on a colalborative filtering (user-based), for recommending shopping products.
You have then a rating matrix (user-item).
Suppose that one of the product is no more available. 
Can we delete this item from the rating matrix ? or what can we do to make it no more used.
For me, i think that the suppression of item from the rating matrix could have a negative impact, because it could introduce a gap in the matrix, and by the way, the user-to-user algorithm will be compromised.
Thanks.
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So . . . . your problem is your data schema . . . . the max quantity of 1 effectively claims that everything is unique which makes the concept of comparison nonsensical and  literally impossible.
I'm not quite sure *why* you have a table of items (Amazon certainly doesn't) but that is not what you should be running your recommender off of.  You should be running it off of an itemTYPE table (which would be a foreign key for your items table if such is truly necessary) -- which is pretty much what Behzad suggested.
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Which datasets are you exploiting to evaluate recommender systems?
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There are multiple datasets on web which someone can use during evaluation step. Depending on you model and the auxiliary information used (tags, timestamps, ratings, etc.) you should choose the best dataset close to your needs. The most used datasets in literature can be found here:
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I am looking to implement a recommender system using ontology. I have built the ontology using protege for elearning. I have a recommendation algorithm in paper. I want to know the implementation language. what language and library should I use? I have a web interface in PHP, it seems like I have to use SPARQl to query the ontology. Any other approach?
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You can follow some base authors in this field
Jena API would help you for writing a java code while interacting the RDF data through SPARQL querying.
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We are developing content based filtering algorithms with application to e-learning and we want to test them on real data set in order to compare with the existing content based filtering. The data published so far that related to education are private. I am looking for public data for learning objects recommendation like Movielens in order to do a comparative study.
Thanks 
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Please, check some information here: http://adlnet.gov/learning-registry/ OR here: http://learningregistry.org/educators/ 
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Hello,
Currently, I am working on building predictive analytics model to predict early diabetes by comparing healthy person habits and symptoms with diabetic patients.There will be few other components of my prediction (that I can not share right now) I am certain that my model and algorithms will work perfectly and provide an accurate result. As you know, data is an important part for the accuracy of the model. Therefore, I need the dataset for training and testing of my model. I have already built the concept and mechanism to provide real time prediction. I would appreciate if someone can help me getting the dataset that includes good amount attributes.
Regards,
Shail
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Devin: I think behavioral data could be crucial because it shows more insight about the patient than analyzing medical data. I read one interesting case study about leading healthcare provider. They were finding real pain points behind increased readmission. After analysis of unstructured and structured data, they found that leading causes of readmission are behavioral such as loneliness, smoking, no exercise, etc.
If you can provide me some sample data, then I would like to add them into the predictive model that I am building to predict diabetes in advance.
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I'm building a recommendation systems that suggests items to a user based on items chosen by similar users. It's similar to collaborative filtering, although I am using multiple dimensions to describe the similarity between two users (excluding the similarity of their previous choices).
I am looking for advice/references regarding how this problem has been solved in the past. Specifically, I am interested in strategies of how to combine multiple similarity features to form a single recommendation.
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I think you need to dig into graph matching problems
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thanks
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Weka works with these metrics: support, confidende, lift, leverage and conviction.
The default output is confidence, take a look:
Best rules found:
  1. 1. biscuits=t frozen foods=t fruit=t total=high 788 ==> bread and cake=t 723 conf:(0.92)
  2. 2. baking needs=t biscuits=t fruit=t total=high 760 ==> bread and cake=t 696 conf:(0.92)
  3. 3. baking needs=t frozen foods=t fruit=t total=high 770 ==> bread and cake=t 705 conf:(0.92)
  4. 4. biscuits=t fruit=t vegetables=t total=high 815 ==> bread and cake=t 746 conf:(0.92)
  5. 5. party snack foods=t fruit=t total=high 854 ==> bread and cake=t 779 conf:(0.91)
  6. 6. biscuits=t frozen foods=t vegetables=t total=high 797 ==> bread and cake=t 725 conf:(0.91)
  7. 7. baking needs=t biscuits=t vegetables=t total=high 772 ==> bread and cake=t 701 conf:(0.91)
  8. 8. biscuits=t fruit=t total=high 954 ==> bread and cake=t 866 conf:(0.91)
  9. 9. frozen foods=t fruit=t vegetables=t total=high 834 ==> bread and cake=t 757 conf:(0.91)
  10. 10. frozen foods=t fruit=t total=high 969 ==> bread and cake=t 877 conf:(0.91)
But you can import weka.jar in our java code and show the others.
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I downloaded music datasets(last,fm, music mellion song, echonest, musicbrains,...), but none of them have information about music's genr. I don't know how to get this field.
please help me about it
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The biggest music database is The Echo Nest (http://developer.echonest.com/docs/v4).
Here you have an API and you are able to request all the metadata for a special song (e.g. beats per minute, genre, duration, valence, energy, ...). Otherwise you can request songs with special attributes, like genre = pop or beats per minute > 150. All you have to do is to request a developer key, which is for free for research purposes.
Here are some example queries:
Here you can find a paper about The Echo Nest: http://ismir2011.ismir.net/papers/OS6-1.pdf
Regards
Patrick
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According to the research, I should design and introduce a web-based recommender system in the field of tourism that its case study is Iran. MyMediaLite library is used as engine of this RS. The following items as factors affecting the response of the system is used:
  • User rating of places
  • User emotional feedback of places (Like/Dislike)
  • The number of trips to each place (places are grouped by type of attraction)
  • Personal characteristics (e.g. gender, blood type, etc.)
  • Using of psychological tests for personality user
The system must be able to produce three kind of suggests:
  1. Sorted suggests based on user features while navigating the site
  2. Suggest other locations when viewing the details of a place
  3. Suggest a tour based on user interests and features, budget, start and end date (also traveling transportation if the travel is intercity)
Suggest one and two is generated by using direct and indirect filters such as user info (profile) and similar users. Suggest three has 4 steps to get the answer:
  1. Organizing a matrix 60(tourists) x 60(places) and adopts item-based mechanism to process recommendation
  2. Select Top 30 sets
  3. Applying genetic algorithm to search the sets of places to satisfy the budget and time restriction
  4. Applying genetic algorithm to schedule the tour path
My phases of generating suggests and algorithm is suitable?
This RS can be a hybrid system?
Would you express your views to improve the system?
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There is a research paper in Procedia, Elsevier 2014 focusing on the app for "A Tourist guide with a recommender system and Social INteraction" This paper discusses the various challenges of recommender system and has performed various types of testing to evaluate the effects of proposed approach. Might be you can find something useful in it.
The approach used for third suggest is quite nice i must admit.
User emotional feedback which is based on LIKE /Dislike can be upgraded to include sentiment analysis of the comments written by user. Finding the sentiment out of comment can provide us with more information about the level of satification rather than like or dislike. Sentiment Analysis could be performed with the help of Deep Learning. This work has been undertaken by researchers at MIT. You can take their reference and embed the same in your work to optimise the results of recommendation. 
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I am in search of a good standard dataset relevant for Music Recommendation systems which should consist of music with places and tags.
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Hello Sapan, if you're still looking for it, you may want to take a look at these resources:
Best,
Danilo
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how i can determine the sense of an ambiguous tag based correlations between feature or number of attributes of items?
i want to find similarity between items based on correlation between attributes of items. which algorithm at weka can help me? thanks  
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I suggest to perform a search using the following keywords:
entity resolution classification
or
entity resolution clustering
If you are not really bound to Weka (and so to classification or a clustering approach) there are plenty of methods to deal with the problem, which to my understanding seems to be an entity resolution (i.e. disambiguation) issue.
Hope it helps :)
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Please provide the best algorithms and tools.
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You can try these tool-kits:
Java based: Lenskit, Mahout, EasyRec, PREA and Rapid Miner
C/C++ based: SVD Feature, CofiRank, GraphLab, LibFM, LibMF, Waffles
Python based: Grab, Python-recsys
Others: MyMediaLite, Recommendable, Recommenderlab
You can search in Google for more details.
Best regards.
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I'm working on using Recommender System (RS) in E-Tourism with personalization approach that my case study is Iran. Some systems like WebGuide, Tainan City, and SigTur/E-Destination were reviewed. I need more info and advice based on (your) experiences and knowledge.
Well, which type of RS could be better for E-Tourism (Collaborative, Content-Based, Hybrid, or etc.)?
How can I improve and increase the factor of the personalization for answering?
What's your suggestion for evaluating final system answer(s)? 
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Collaborative Filtering is often employed in Point-of-Interest (POI) recommendation, but several issues must be tackled before using the traditional approach. Information about visited POIs by users is often partial (data sparsity) and usually the number of POIs is very large in comparison with the user ratings for each POI. Additional information about POIs is always welcome (category, tags, reviews, number of visits, etc). Luckily, tourist attractions such as museums, galleries, fountains, are quite "stable" temporally so it is easier to implement a CF approach. Personalisation needs information about user preferences, so it is crucial understand how this info can be collected in your scenario (through mobile devices, web sites, purchase histories). There are several papers online, you can have a look at this survey:
A Survey on Recommendations in Location-based Social Networks
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I'm looking for a state of the art dataset to evaluate a content-based context-aware recommendation framework.
A collaborative dataset is ok as well, I could scrape content from DBPedia or other online sources.
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if you are looking for I recommend dataset http://archive.ics.uci.edu/ml/
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Dear all,
We developed a context-aware content-based recommender system which computes recommendations by calculating the similarity between user profile and items description.
Given that the recommendations are built according to their descending cosine similarity scores (the higher the similarity, the better the recommendations), we evaluated the effectiveness of the recommender by adopting typical classification measures, as precision and recall.
In order to extend the evaluation of our system and to compare it with newer and better approaches, we would like to "shift" towards a recommendation model which predicts ratings, in order to evaluate it through more widespread measures such as RMSE and MAE.
How would you deal with this? How can we shift from a similarity score (e.g. 0,85) to a rating predicition (e.g. 4,21)? 
Thank you all!
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I think that shifting from recommendation to prediction may not be that trivial: generating predictions implies predicting how much a user will like/find useful a given item, while content-based recommendation is more of an information filtering affair.
By multiplying the cosine similarity and calling it a prediction you are assuming that the percieved quality of an item is solely based upon its similarity with other items. There is plenty of examples in which such an assumption won't hold: consider for instance Horror movies, most of them have the same plot, the same situations and often the very same actors and directors, however despite the high similarity you can still find good horror movies and crappy horror movies.
On the other hand, there exists other domains in which the potential interest can be represented by the sole similarity, for instance the domain of Scientific literature, Legal papers, Patents, and other technical documentation (when writing a paper you may cite papers that you consider crap, or a lawyer may find useful laws he/she just doesn't like), and therefore in which such shifting operation may make much more sense.
So, in my humble opinion, everything boils down at asking yourself what are you recommending: if your system recommends papers, well, the "naive" multiplication solution may just work fine, otherwise consider introducing collaborative filtering elements to predict ratings.
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In the collaborative filtering algorithm based on memory k-nn, k selects a number of users. For predicting that number, k can be neither too small nor too big, as I would select that number to get a good recommendation.
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example:
from sklearn.neighbors import KNeighborsClassifier
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import accuracy_score, classification_report
# here must be some code for your training set and test set
tuned_parameters = [{'n_neighbors': [1,2,3],
'weights': ['distance', 'uniform'],
'algorithm': ['ball_tree', 'kd_tree', 'brute']}]
scores = ['precision', 'recall']
performances = [];
for score in scores:
print("# Tuning hyper-parameters for %s" % score)
print()
clf = GridSearchCV(KNeighborsClassifier(), tuned_parameters, cv=10, scoring=score)
clf.fit(X_train, y_train)
print("Best parameters set found on development set:")
print()
print(clf.best_estimator_)
print()
print("Grid scores on development set:")
print()
for params, mean_score, scores in clf.grid_scores_:
print("%0.3f (+/-%0.03f) for %r"
% (mean_score, scores.std() / 2, params))
print()
print("Detailed classification report:")
print()
print("The model is trained on the full development set.")
print("The scores are computed on the full evaluation set.")
print()
y_true, y_pred = y_test, clf.predict(X_test)
print(classification_report(y_true, y_pred))
print()
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I had been searching about the topic but can’t find anything specific about it, the ones that always pop up are k-nn, genetic algorithms, bayesians networks, clustering and decision trees. So after a while I was wondering if is really viable the use of agents, Q learning for content suggestion? Thanks in advanced.
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.
not exactly _content_ suggestion but quite close, see "dueling bandits" by Y. Yue and T. Joachims
the point is that content recommendation is almost always contextual : users'profile information is often more important than the overall success rate of an item (otherwise, keep recommending blockbusters and you're fine !)
this brings you to "contextual bandits", "bandits with side information" and so on ...
by the way, googling _Reinforced Learning for content recommendation_ will lead you to quite a crowd of interesting publications !
.
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