A Hybrid Recommender System: User Profiling from Keywords and Ratings

Over the last decade, user-generated content has grown continuously. Recommender systems that exploit user feedback are widely used in e-commerce and quite necessary for business enhancement. To make use of such user feedback, we propose a new content/collaborative hybrid approach, which is built on top of the recently released hetrec2011-movielens-2k dataset and is an extension of a previously proposed neighborhood based approach, called Weighted Tag Recommender (WTR). Our approach has two versions. Both versions make use of ratings to enable collaborative filtering and use either user tags, available in the hetrec2011-movielens-2k dataset, or movie keywords retrieved from IMDB, to capture movie content information. Experimental results show that the information from keywords can help build a movie recommender system competitive with other neighborhood based approaches and even with more sophisticated state-of-the-art approaches.