Rating LDA model for collaborative filtering

People are pleased with the great wealth of products in online stores. However, it is more and more difficult for people to choose their favorite products in an online store. Thus, recommendation systems are necessary to provide useful suggestions and selections. A user's choice is not only influenced by his/her interests, but also by the ratings of others. In this paper, we propose a Rating LDA (RLDA) Model for collaborative filtering by adding rating information to the Latent Dirichlet Allocation (LDA). User behavior is not independent; it follows the trend of others. Therefore, we assume that for similar interests, the higher the proportion of high ratings, the more popular the items. We perform experiments on two real world data sets: MovieLens100k and MovieLens1M. Results show that, in terms of F1 score, our proposed approach significantly outperforms some baseline methods.

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