A hybrid recommender: user profiling from tags/keywords and ratings - Nagar, Swapnil (Kansas State University, 2012)
Over the last decade, the Internet has become an involving medium and user-generated content is continuously growing. 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 approach, called Weighted Tag Recommender (WTR). The WTR approach makes use of tag information available in hetrec2011-movielens-2k, but it does not use explicit ratings. As opposed to WTR, our modified approach can make use of ratings to capture collaborative filtering and either user-tags, available in the hetrec2011-movielens-2k, or movie keywords retrieved from IMDB, to capture movie content information. We call the two versions of our approach Weighted Tag Rating Recommender (WTRR) and Weighted Keyword Rating Recommender (WKRR), respectively. Movie keywords (which are not user specific) allow us to use all ratings available in hetrec2011-movielens-2k, as WKKR associates the content information from movies with the users, based on their ratings. On the other hand, tags provide more specific information for a user, but limit the usage of the data to the user-movie pairs that have tags (significantly smaller number compared with all pairs that have ratings). Both our keyword and tag representations of users can help alleviate the noise and semantic ambiguity problems inherent in information contributed by users of social networks. Experiments using the WTRR approach on a subset of the dataset (which contains both ratings and tags) show that it slightly outperforms the WKRR approach. However, WKRR can be applied to the whole hetrec2011-movielens-2k dataset and 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.
Keywords: Tagging Recommender; Recommender; Keyword Recommender; Weighted Tag; Weighted Keyword; Recommender using explicit ratings; Computer Science (0984)