I-CARS: An Interactive Context-Aware Recommender System

Context-aware recommendation has attracted significant attentions over online sites due to its smart context adaption in improving recommendation quality. However, the user's instant contexts do not follow his/her regular user behaviour patterns, thus have not been well captured for advanced personalization of recommendation generation. In this work, we propose an Interactive Context-Aware Recommender System (I-CARS), which allows users to interact and present their needs, so the system can personalize and refine user preferences. I-CARS iteratively asks a question to a user to trigger feedback in term of her recent contexts and incorporates the response to recommend items most likely satisfying his/her instant interests. Specifically, we first propose a Personalized Weighted Context-Aware Matrix Factorization (PW-CAMF) that enables the personalization of important contexts for each user. Then we propose two question selection strategies that exploit user preferences through feedback. We have conducted comprehensive experiments over two real datasets. The experimental results prove the effectiveness of our I-CARS system compare to existing competitors.