Zenith seminar “A Distributed Collaborative Filtering Algorithm With Multiple and Heterogeneous Data Sources”, Mohamed Reda Bouadjenek, October 10, 2014

Reda will present his recent work in Distributed Collaborative Filtering on Friday 10 Oct at 3:30pm (room to be defined). A Distributed Collaborative Filtering Algorithm With Multiple and Heterogeneous Data Sources. Recommender systems are used as a mean to supply users with content that may be of interest to them. They have attracted the attention of the research community, and have become a popular research topic, where many aspects and dimensions have been studied to make them more accurate and effective (this includes the: social dimension, geographical dimension, diversification aspect, etc.). Collaborative filtering (CF) is certainly one of the most famous recommendation methods, which consists in predicting whether, or how much, a user will like (or dislike) an item by leveraging knowledge of that user’s preferences as well as those of other users. However, in practice, users interact and express their opinion on only a small subset of items, which makes the corresponding user-item rating matrix very sparse. Consequently, in a recommender system, this data sparsity induced mainly two problems: (1) the lack of data to effectively model users’ preferences (news users suffer from the cold-start problem), and similarly (2) the lack of data to effectively model items’ preferences (new items suffer from the cold-start problem since no user has rated them). However, on the other hand, users use many online services, which can provide information about their interest and the content of items (e.g. Google search engine, Facebook, Twitter, etc). These services may be valuable data sources, which supply information to help a recommender system in modeling users and items’ preferences, and thus, make the recommender system more precise. Moreover, these data sources are distributed, and geographically distant from each other, which raise many research problems and challenges to design a distributed recommendation algorithm. Hence, in this talk, we present a new distributed collaborative filtering algorithm, which exploits and combine these multiple and heterogeneous data sources to improve the recommendation quality. Short bio: Reda Bouadjenek received a master and a PhD degree in computer science from the University of Versailles, France, in 2009 and 2013 respectively. He is currently a postdoctoral researcher at INRIA, and works on recommender systems. Previously, he worked for Alcatel-Lucent Bell Labs France from 2010 to 2013 as researcher, then was a visitor researcher at NICTA&ANU, Australia, in 2013. His research interests include Information Retrieval, Social Network Analysis, Data Mining, Machine Learning, Recommender Systems, and Databases.