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An Efficient Collaborative Filtering and Graph Approach for Business-Matching Systems
Abstract
The unprecedented availability of Internet and Social media led to significant changes in daily business tasks and enforced managers to get a new insight into the way of supervising organizations. Several managers and participants from all the world come to look for business opportunities to enhance the quality of work in such trade exhibitions. Hence, they implement Business-Matching based recommendation system to help attendees to find the appropriate future partner for their business, increase user’s interaction and enrich shopping potential. Recommendation System should be the backbone of any business matching concept. Since it has the potential to change the way that websites communicate with users and allows companies to maximize their Return On Investment (ROI) based on the information they can gather on each customer’s preferences and purchases. In this paper, we present a taxonomy of Business Matching features based on recommendation systems, approaches, actions, and goals, and according to these variables, we study the existing literature on recommender systems applied in different fields.
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Modern lifestyles have been dramatically changed by recent ICT development. As for the marketing field, enterprises tend to use video advertising because of its effectiveness. On the other hand, the number of content producers increases these days, since new work phenomenon the ‘YouTuber’ appears as strong medium for advertising. Therefore, both demand and supply are increasing in this video advertising field. This paper analyzed such trend of increasing situation and proposed new type of business model including matching function architecture between enterprises and content producers for video advertising. Secondly, matching algorithm based on the calculation of relativity between them is proposed and this feasibility is evaluated.
Collaborative filtering method was widely used in the recommendation system. This method was able to provide recommendations to the user through the similarity values between users. However, the central issues in this method were new user issue and sparsity. This paper discusses about how to use matrix factorization and nearest-neighbour in film recommendation systems. Both of methods will be used in order to make more accurate recommendations. Based on the experiments results, the combination of matrix factorization and classical collaborative filtering (nearest neighbor) could improve the prediction accuracy. It can be concluded that the combination of matrix factorization and nearest-neighbor produced a better prediction accuracy
Although Collaborative Filtering (CF) -based recommender systems have received great success in a variety of applications, they still under-perform and are unable to provide accurate recommendations when users and items have few ratings, resulting in reduced coverage. To overcome these limitations, we propose an effective hybrid user-item trust-based (HUIT) recommendation approach in this paper that fuses the users' and items' implicit trust information. We have also considered and computed user and item global reputations into this approach. This approach allows the recommender system to make an increased number of accurate predictions, especially in circumstances where users and items have few ratings. Experiments on four real-world datasets, particularly a business-to-business (B2B) case study, show that the proposed HUIT recommendation approach significantly outperforms state-of-the-art recommendation algorithms in terms of recommendation accuracy and coverage, as well as significantly alleviating data sparsity, cold-start user and cold-start item problems.
The goal of this research is to define and capture a series of parameters that allowed us to perform a comparative analysis and find correlations between explicit and implicit feedback on recommender systems. Most of these systems require explicit actions from the users, such as rating, and commenting. In the context of electronic books this interaction may alter the patterns of reading and understanding of the users, as they are asked to stop reading and rate the content. By simulating the behavior of an electronic book reader we have improved the feedback process, by implicitly capturing, measuring, and classifying the information needed to discover user interests. In these times of information overload, we can now develop recommender systems that are mostly based on the user’s behavior, by relying on the obtained results.
Among other conceptualizations, smart cities have been defined as functional urban areas articulated by the use of Information and Communication Technologies (ICT) and modern infrastructures to face city problems in efficient and sustainable ways. Within ICT, recommender systems are strong tools that filter relevant information, upgrading the relations between stakeholders in the polity and civil society, and assisting in decision making tasks through technological platforms. There are scientific articles covering recommendation approaches in smart city applications, and there are recommendation solutions implemented in real world smart city initiatives. However, to the best of our knowledge, there is not a comprehensive review of the state of the art on recommender systems for smart cities. For this reason, in this paper we present a taxonomy of smart city features, dimensions, actions and goals, and, according to these variables, we survey the existing literature on recommender systems. As a result of our survey, we do not only identify and analyze main research trends, but also show current opportunities and challenges where personalized recommendations could be exploited as solutions for citizens, firms and public administrations.
This work focuses on finding an optimal
solution based on Machine Learning, for computation of
a matching score between a Buyer and Seller, for
business-to-business event match purposes. The idea is to
propose to the Seller the most relevant Buyers who can
match the most with him when attending a trade show
event. Currently, the match propensity is based on the
syntactic analysis of the interests of the Buyer with the
industries of all Sellers and propose only those who have
at least one similarity with the Seller's industries. This
process is manual and primitive. The customized model
involving Interactions Converter Algorithm,
Collaborative Filtering and Natural Language
Processing, is the proposed solution in this work. This
approach is based on implicit feedback derived from the
user's interaction on front-end application. The user's
behavior is tracked through several indicators that are
used to provide an explicit rating for the material
interacted with. The integration of this solution will allow
matching score improvement in business-to-business
match forecasting, resulting in better planning for Sellers
and Buyers and more effective meetings.
Recommending individuals through keywords is an essential and common search task in online social platforms such as Facebook and LinkedIn. However, it is often that one has only the impression about the desired targets, depicted by labels of social contexts (e.g. gender, interests, skills, visited locations, employment, etc). Assume each user is associated a set of labels, we propose a novel task, Search by Social Contexts (SSC), in online social networks. SSC is a kind of query-based people recommendation, recommending the desired target based on a set of user-specified query labels. We develop the method Social Query Embedding Learning (SQEL) to deal with SSC. SQEL aims to learn the feature representation (i.e., embedding vector) of the query, along with user feature vectors derived from graph embedding, and use the learned query vectors to find the targets via similarity. Experiments conducted on Facebook and Twitter datasets exhibit satisfying accuracy and encourage more advanced efforts on search by social contexts.
In the last ten years, social networks have had a great influence on people’s lifestyles and have changed, above all, the way users communicate and relate. This is why, one of the main lines of research in the field of social networks focuses on finding and analyzing possible connections between users. These developments allow users to expand on their network of contacts without having to search among the total set of users. However, there are many types of social networks which attract users with specific needs, these needs influence on the type of contacts users are looking for. Our article proposes a relationship recommender system for a business and employment-oriented social network. The presented system functions by extracting relevant information from the social network which it then uses to adequately recommend new contacts and job offers to users. The recommender system uses information gathered from job offer descriptions, user profiles and users’ actions. Then, different metrics are applied in order to discover new ties that are likely to convert into relationships.
Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximally preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work address these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques and application scenarios.
This paper proposes an intelligent recommendation system that has increasing promotion effect. Normally, intelligent recommendation system suggests suitable product to consumer but the Intelligent Recommendation System recommends fit well-known person to company for increasing promotion effect. It uses weight based algorithm and matching model based on well-known person relation graph.
There are a large number of items with users' ratings on the internet. Instead of predicting items' ratings with the item content or the user-item-rating matrix, this paper proposes an approach to predict items' ratings basing on paired comparisons among contents of items with different ratings. We assume that the user interest on each item can be represented by the combination of different content features, and employ Bradley-Terry model to confirm the user interest value of each feature. Experimental results show that the competition-based recommender system outperforms popular approaches.
This paper is concerned with the evaluation and tabulation of certain integrals of the type I(mu , nu ; lambda ) = int0∞ Jmu(at) Jnu(bt) e-ct tlambdadt. In part I of this paper, a formula is derived for the integrals in terms of an integral of a hypergeometric function. This new integral is evaluated in the particular cases which are of most frequent use in mathematical physics. By means of these results, approximate expansions are obtained for cases in which the ratio b/a is small or in which b ~= a and c/a is small. In part II, recurrence relations are developed between integrals with integral values of the parameters mu , nu and lambda . Tables are given by means of which I(0, 0; 1), I(0, 1; 1), I(1, 0; 1), I(1, 1; 1), I(0, 0; 0), I(1, 0; 0), I(0, 1; 0), I(1, 1; 0), I(0, 1; -1), I(1, 0; -1) and I(1, 1; -1) may be evaluated for 0
Relationship recommender system in a business and employment-oriented social network
- P Chamoso
- A Rivas
- S Rodriıguez
- J Bajo
node2vec: Embeddings for Graph Data
- E Cohen
Graph Embeddings - The Summary [WWW Document
- P Godec
Overview of Recommender Algorithms - Part 1. A Practical Guide to Building Recommender Systems
- H Maya
Use Cases of Recommendation Systems in Business - Current Applications and Methods
- C Underwood