Mehdi Elahi

Mehdi Elahi
University of Bergen | UiB · Department of Information Science and Media Studies

Doctor of Philosophy (Ph.D.) in Computer Science
WP Leader at MediaFutures center

About

100
Publications
71,110
Reads
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1,972
Citations
Introduction
Mehdi Elahi is an Associate Professor at University of Bergen (Norway). He received M.Sc. degree in Electrical Engineering (Sweden) in 2010, and Ph.D. degree in Computer Science (Italy) in 2014. For more than 3 years, he has been serving as an Assistant Professor at Free University of Bozen - Bolzano (Italy), where has researched on various aspects of Recommender Systems. Latest news: https://twitter.com/mehdielaahi
Additional affiliations
January 2020 - April 2020
University of Bergen
Position
  • Professor (Associate)
Description
  • Research on Recommender Systems , Teaching different courses, Grant writing
October 2016 - December 2019
Free University of Bozen-Bolzano
Position
  • Lecturer
Description
  • Programming for Data Analytics
March 2015 - September 2017
Politecnico di Milano
Position
  • Lecturer
Description
  • Recommender Systems course: Content-based, Collaborative Filtering (CF), user-based CF, item-based CF, Matrix Factorization, Evalution, Cold start, Personality-based, Decision Making
Education
January 2010 - January 2014
Free University of Bozen-Bolzano
Field of study
  • Computer Science

Publications

Publications (100)
Article
Full-text available
The accuracy of collaborative-filtering recommender systems largely depends on three factors: the quality of the rating prediction algorithm, and the quantity and quality of available ratings. While research in the field of recommender systems often concentrates on improving prediction algorithms, even the best algorithms will fail if they are fed...
Article
Full-text available
The new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which...
Chapter
Full-text available
In Recommender Systems (RS), a users preferences are expressed in terms of rated items, where incorporating each rating may improve the RS’s predictive accuracy. In addition to a user rating items at-will (a passive process), RSs may also actively elicit the user to rate items, a process known as Active Learning (AL). However, the number of interac...
Article
Full-text available
Design Patterns (DPs) are acknowledged as powerful conceptual tools to improve design quality and to reduce time and cost of the development process by effect of the reuse of “good” design solutions. In many fields (e.g., software engineering, web engineering, interface design) patterns are widely used by practitioners and are also investigated fro...
Article
The most popular techniques in Recommender Systems are based on Collaborative Filtering (CF) approaches. Classical CF learns from similarities among the rating data to predict the missing ratings in generating recommendations. While user ratings are very informative signals, other sources of user data can contribute to the prediction of user prefer...
Article
Full-text available
A challenge for many young adults is to find the right institution to follow higher education. Global university rankings are commonly used, but inefficient tool, for they do not consider a person's preferences and needs. For example, some persons pursue prestige in their higher education, while others prefer proximity. This paper develops and eval...
Article
Full-text available
Reading or viewing recommendations are a common feature on modern media sites. What is shown to consumers as recommendations is nowadays often automatically determined by AI algorithms, typically with the goal of helping consumers discover relevant content more easily. However, the highlighting or filtering of information that comes with such recom...
Article
General recommenders and sequential recommenders are two modeling paradigms of recommender. The main focus of a general recommender is to identify long-term user preferences, while the user’s sequential behaviors are ignored and sequential recommenders try to capture short-term user preferences by exploring item-to-item relations, failing to consid...
Article
Full-text available
The last two decades have witnessed major disruptions to the traditional media industry as a result of technological breakthroughs. New opportunities and challenges continue to arise, most recently as a result of the rapid advance and adoption of artificial intelligence technologies. On the one hand, the broad adoption of these technologies may int...
Preprint
Full-text available
When a movie is uploaded to a movie Recommender System (e.g., YouTube), the system can exploit various forms of descriptive features (e.g., tags and genre) in order to generate personalized recommendation for users. However, there are situations where the descriptive features are missing or very limited and the system may fail to include such a mov...
Article
Full-text available
Recommender Systems are decision support tools that adopt advanced algorithms in order to help users to find less-explored items that can be interesting for them. While recommender systems may offer a range of attractive benefits, they may also intensify undesired effects, such as the Popularity Bias, where a few popular users/items get more popula...
Article
Full-text available
Recommender Systems are decision support tools that adopt advanced algorithms in order to help users to find less-explored items that can be interesting for them. While recommender systems may offer a range of attractive benefits, they may also intensify undesired effects, such as the Popularity Bias, where a few popular users/items get more popula...
Preprint
Full-text available
Modern Recommendation Systems will require to access and understand the big data built on top of the large data islands. This is important as the growing enhancement in interconnection, storage, as well as data management has made it possible to connect to data deluge from the big data, which in turn, can lead to making intelligent and accurate per...
Chapter
Full-text available
Modern Recommendation Systems will require to access and understand the big data built on top of the large data islands. This is important as the growing enhancement in interconnection, storage, as well as data management has made it possible to connect to data deluge from the big data, which in turn, can lead to making intelligent and accurate per...
Conference Paper
Full-text available
This paper addresses the so-called New Item problem in video Recommender Systems, as part of Cold Start. New item problem occurs when a new item is added to the system catalog, and the recommender system has no or little data describing that item. This could cause the system to fail to meaningfully recommend the new item to the users. We propose a...
Chapter
Full-text available
From the early years, the research on recommender systems has been largely focused on developing advanced recommender algorithms. These sophisticated algorithms are capable of exploiting a wide range of data, associated with video items, and build quality recommendations for users. It is true that the excellency of recommender systems can be very m...
Article
Full-text available
Over the past years, the research of video recommender systems (RSs) has been mainly focussed on the development of novel algorithms. Although beneficial, still any algorithm may fail to recommend video items that the system has no form of data associated to them (New Item Cold Start). This problem occurs when a new item is added to the catalogue o...
Chapter
Full-text available
With the rapid growth of online market for clothing, footwear, hairstyle, and makeup, consumers are getting increasingly overwhelmed with the volume, velocity and variety of production. Fashion Recom� mender Systems can tackle with choice overload by suggesting the most interesting products to the users. However, recommender systems are unable to g...
Article
Internet of Things (IoT) is composed of physical devices, communication networks, and services provided by edge systems and over-the-top applications. IoT connects billions of devices that collect data from the physical environment, which are pre-processed at the edge and then forwarded to processing services at the core of the infrastructure, on t...
Article
Full-text available
From the early years, the research on recommender systems has been largely focused on developing advanced recommender algorithms. These sophisticated algorithms are capable of exploiting a wide range of data, associated with video items, and build quality recommendations for users. It is true that the excellency of recommender systems can be very m...
Conference Paper
Full-text available
The rise in international migration over the past decades has given more audience to this crucial issue of human life. According to reports by United Nations, more than 243 million people live in a country that is not their place of birth. People decide to immigrate, based on a range of reasons, and choose the country of destination with the hope t...
Conference Paper
Full-text available
Recommender Systems (RSs) have become essential tools in any video-sharing platforms (such as YouTube) by generating video suggestions for users. Although, RSs have been effective, however, they suffer from the so-called New Item problem. New item problem, as part of Cold Start problem, happens when a new item is added to the system catalogue and t...
Conference Paper
Full-text available
The major focus of recommender systems (RSs) research is on improving the goodness of the generated recommendations. Less attention has been dedicated to understand the effect of an RS on the actual users' choices. Hence, in this paper, we propose a novel simulation model of users' choices under the influence of an RS. The model leverages real rati...
Chapter
Full-text available
One of the many components used in biometrics is optical flow estimation. This could be due to the fact that motion is an inseparable attribute of our (visual) world and hence it is a valuable resource of data needed to tackle many real-world problems. Indeed, technologies that use object detection, motion detection, object tracking, gait recogniti...
Conference Paper
Full-text available
Optical Flow (OF) estimation is an important task which could be effectively used for a variety of Computer Vision (CV) applications. While a range of techniques have been already proposed, however accurately estimating the OF is still a very challenging task. The most recent approaches for OF estimation exploit advanced Deep Learning techniques wh...
Article
Full-text available
In this paper we introduce a novel model for simulating the choice making procedure of users under the influence of a Recommender System (RS). Our model leverages the knowledge of users' preferences and simulates repeated choices. We investigate the evolution of these simulated choices in the presence of different RSs and analyse their impact on th...
Conference Paper
Full-text available
We present a demo application of a web-based recommender systems that is powered by the "Movie Genome", i.e., a rich semantic description of a movie's content, including state-of-the-art audio and visual descriptors and metadata (genre and tags). The current version of the Movie Genome web application implements content-based filtering approaches....
Preprint
Full-text available
In Movie Recommender Systems, when a new user registers to the system and she has not yet provided any information about her, the system may not be able to generate personalized recommendations for that user. In such a Cold Start situation, many real-world recommender systems suggest popular movies to the new user. Such movies are very likely to be...
Preprint
Full-text available
In this paper, we explore the potential of using visual features in movie Recommender Systems. This type of content features can be extracted automatically without any human involvement and have been shown to be very effective in representing the visual content of movies. We have performed the following experiments, using a large dataset of movie t...
Data
MA14KD [AGGREGATED] dataset ("Movie Atract 14K Dataset") provides a set of 181 aggregated VISUAL features extracted from more than 14000 movie and tv series trailers. We measured the “Attractiveness” of the every frame of the movie trailers according to a paper by San Pedro, Jose, and Stefan Siersdorfer and extracted the described features from mov...
Method
Full-text available
MA14KD [AGGREGATED] dataset ("Movie Atract 14K Dataset") provides a set of 181 aggregated VISUAL features extracted from more than 14000 movie and tv series trailers. We measured the “Attractiveness” of the every frame of the movie trailers according to a paper by San Pedro, Jose, and Stefan Siersdorfer and extracted the described features from mov...
Data
MA14KD [ORIGINAL] dataset ("Movie Atract 14K Dataset") provides a set of 10 VISUAL features extracted from more than 14000 movie and tv series trailers. We measured the “Attractiveness” of every frame of the movie trailers according to a paper by Jose San Pedro, and Stefan Siersdorfer and extracted the described features from movie trailers.
Method
Full-text available
MA14KD [ORIGINAL] dataset ("Movie Atract 14K Dataset") provides a set of 10 VISUAL features extracted from more than 14000 movie and tv series trailers. We measured the “Attractiveness” of every frame of the movie trailers according to a paper by Jose San Pedro, and Stefan Siersdorfer and extracted the described features from movie trailers.
Preprint
Full-text available
Recommendation systems are essential tools to overcome the choice overload problem by suggesting items of interest to users. However, they suffer from a major challenge which is the so-called cold-start problem. The cold-start problem typically happens when the system does not have any form of data on new users and on new items. In this chapter, we...
Conference Paper
Full-text available
Users of a recommender system may be requested to express their preferences about items either with evaluations of items (e.g. a rating) or with comparisons of item pairs. In this work we focus on the acquisition of pairwise preferences in the music domain. Asking the user to explicitly compare music, i.e., which, among two listened tracks, is pref...
Chapter
Full-text available
The Internet of Things (IoT) enables new ways for exploiting the synergy between the physical and the digital world and therefore promises a more direct and active interaction between tourists and local products and places. In this article we show how, by distributing sensors/actuators in the environment or attaching them to objects, one can sense,...
Article
Full-text available
Item features play an important role in movie recommender systems, where recommendations can be generated by using explicit or implicit preferences of users on traditional features (attributes) such as tag, genre, and cast. Typically, movie features are human-generated, either editorially (e.g., genre and cast) or by leveraging the wisdom of the cr...
Article
Full-text available
Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing subst...
Preprint
Full-text available
Children, nowadays, are great consumers of media for them [6], and there is growing interest towards novel mechanisms that can consider their specific needs and improve both the recommendation process and output of videos for them. Children, in fact, have unique characteristics , which change with age. In particular, in the 8-12 age range, they lik...
Preprint
Full-text available
Preprint of a book chapter published in Book Collaborative Recommendations Algorithms, Practical Challenges and Applications [https://doi.org/10.1142/11131] © [copyright World Scientific Publishing Company] [https://www.worldscientific.com/worldscibooks/10.1142/11131]
Patent
Full-text available
There is disclosed a method for generating movie recommendations, based on automatic extraction of features from a multimedia content, wherein the extracted features are visual features representing mise-en-scène characteristics of the movie defined on the basis of Applied Media Aesthetic theory, said extracted features being then fed to content-ba...
Conference Paper
Full-text available
In recent years, considerable attention has been given to studies on the role of playlists in music consumption. A study carried out in 2016, by the Music Business Association, showed that playlists accounted for 31% of music listening time among listeners in the USA. Another study, conducted by MIDiA, revealed that as many as 55% of streaming musi...
Conference Paper
Full-text available
In the last years, we have seen much attention given to the semantic gap problem in multimedia recommender systems. Much effort has been devoted to bridge this gap by building tools for the extraction of high-level, semantics-based features from multimedia content, as low-level features are not considered useful because they deal primarily with re...
Article
Full-text available
The ACM Recommender Systems Challenge 2017 1 focused on the problem of job recommendations: given a new job advertisement, the goal was to identify those users who are both (a) interested in getting notified about the job advertisement, and (b) appropriate candidates for the given job. Participating teams had to balance between user interests and r...
Conference Paper
Full-text available
We present a research tool for user preference elicitation that collects both explicit user feedback and unobtrusively acquired facial expressions. The concrete implementation is a web-based user interface where the user is presented with two music excerpts. After listening to both, the user provides a pairwise score (i.e. which of the two items is...
Conference Paper
Full-text available
Traditional food recommender systems exploit items' ratings and descriptions in order to generate relevant recommendations for the users. While this data is important, it might not entirely capture the true users' preferences. In this paper, we analyse the performance of a food recommender that allows users to enter their preferences in the form of...
Conference Paper
Recommender systems generate recommendations by analysing which items the user consumes or likes. Moreover, in many scenarios, e.g., when a user is visiting an exhibition or a city, users are faced with a sequence of decisions, and the recommender should therefore suggest, at each decision step, a set of viable recommendations (attractions). In the...
Conference Paper
Full-text available
In order to use and model nutritional knowledge in a food recommender system, uncertainties regarding the users nutritional state and thus the personal health value of food items, as well as conflicting nutritional theories need to be quantified, qualified and subsumed into falsifiable models. In this paper, we reflect on different error sources wi...
Conference Paper
Full-text available
Finding the right university to study is still a challenge for many people due to the large number of universities worldwide. Although there exist a number of global university rankings, they provide non-personalized rankings as one-size-fits-all solution. This becomes an issue since different people may have different preferences and consideration...
Data
This dataset provides a set of 774 low-level VISUAL features extracted from 3964 movie trailers. The movie IDs are in agreement with the movie IDs provided by "MovieLens (ML) dataset" (ML-20M or Full Version as in May 2017). All the movie titles, ratings and associated movie genres and tags can be collected from the MovieLens website. We used the l...
Technical Report
Full-text available
This dataset provides a set of 774 low-level VISUAL features extracted from 3964 movie trailers. The movie IDs are in agreement with the movie IDs provided by "MovieLens (ML) dataset" (ML-20M or Full Version as in May 2017). All the movie titles, ratings and associated movie genres and tags can be collected from the MovieLens website. We used the l...
Article
Full-text available
Item features play an important role in movie recommender systems, where recommendations can be generated by using explicit or implicit preferences of users on traditional features (attributes) such as tag, genre, and cast. Typically, movie features are human-generated, either editorially (e.g., genre and cast) or by leveraging the wisdom of the cr...
Conference Paper
Full-text available
Previous works have shown the effectiveness of using stylistic visual features, indicative of the movie style, in content-based movie recommendation. However, they have mainly focused on a particular recommendation scenario, i.e., when a new movie is added to the catalogue and no information is available for that movie (New Item scenario). However,...