The New Challenges when Modeling Context through Diversity over Time in Recommender Systems

Amaury L'Huillier 1 Sylvain Castagnos 1 Anne Boyer 1
1 KIWI - Knowledge Information and Web Intelligence
LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : The main goal of recommender systems is to help users to filter all the information available by suggesting items they may like without they had to find them by themselves. Although the rating prediction is a pretty well controlled topic, being able to make a recommendation at the right moment still remain a challenging task. To this end, most researches try to integrate contextual information (weather, mood, location of users, etc.) in the recommendation process. Even if this process increases users satisfaction, using personal information faces with users' privacy issues. In a different way, our approach is only giving credits to the evolution of diversity within the recent history of consultations, allowing us to automatically detect implicit contexts. In this paper, we will discuss the scientific challenges to be overcome to take maximum advantage of those implicit contexts in the recommendation process.
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Proceedings of the 24th Conference on User Modeling, Adaptation and Personalization (UMAP 2016 Doctoral Consortium), Jul 2016, Halifax, Canada. 2016, Proceedings of the 24th Conference on User Modeling, Adaptation and Personalization (UMAP 2016 Doctoral Consortium)
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Amaury L'Huillier, Sylvain Castagnos, Anne Boyer. The New Challenges when Modeling Context through Diversity over Time in Recommender Systems. Proceedings of the 24th Conference on User Modeling, Adaptation and Personalization (UMAP 2016 Doctoral Consortium), Jul 2016, Halifax, Canada. 2016, Proceedings of the 24th Conference on User Modeling, Adaptation and Personalization (UMAP 2016 Doctoral Consortium). 〈hal-01306790〉

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