Enhancing Collaborative Filtering Using Implicit Relations in Data

International audienceThis work presents a Recommender System (RS) that relies on distributed recommendation techniques and implicit relations in data. In order to simplify the experience of users, recommender systems pre-select and filter information in which they may be interested in. Users express their interests in items by giving their opinion (explicit data) and navigating through the web-page (implicit data). The Matrix Fac-torization (MF) recommendation technique analyze this feedback, but it does not take more heterogeneous data into account. In order to improve recommendations, the description of items can be used to increase the relations among data. Our proposal extends MF techniques by adding implicit relations in an independent layer. Indeed, using past preferences, we deeply analyze the implicit interest of users in the attributes of items. By using this, we transform ratings and predictions into " semantic values " , where the term semantic indicates the expansion in the meaning of ratings. The experimentation phase uses MovieLens and IMDb database. We compare our work against a simple Matrix Factorization technique. Results show accurate personalized recommendations. At least but not at last, both recommendation analysis and semantic analysis can be par-allelized, alleviating time processing in large amount of data

Cross-domain Recommendations based on semantically-enhanced User Web Behavior

Information seeking in the Web can be facilitated by recommender systems that guide the users in a personalized manner to relevant resources in the large space of the possible options in the Web. This work investigates how to model people\u27s Web behavior at multiple sites and learn to predict future preferences, in order to generate relevant cross-domain recommendations. This thesis contributes with novel techniques for building cross-domain recommender systems in an open Web setting

Ontology-based Web Recommendation from tags

With the advent of social networks and tagging systems, The Internet has recently witnessed a big leap in the use of Web Recommendation Systems WRS. Based on users\u27 likings of items and their browsing history on the world wide web, these systems are able to predict and recommend items and future purchases to users. They are being used now in various domains, like news article recommendation, product recommendation, and make-friend recommendation. WRS are still limited by several problems, of which are sparsity, and the new user problem. They also fail to make full use and harness the power of domain knowledge and semantic web ontologies. In this article, we discuss how an ontology-based WRS can utilize relations and concepts in an ontology, along with user-provided tags, to provide top-n recommendations without the need for item clustering or user ratings. For this purpose, we also propose a dimensionality reduction method based on the domain ontology, to solve the sparsity problem