Abstract: Recommender systems promote sales of products and services by helping users alleviate the information overload problem. Collaborative filtering is most extensively used approach to design recommender system. The main idea of collaborative filtering is that recommendation for each active user is received by comparing with the preferences of other users who have rated the product in similar way to the active user. Matrix factorization technique is one of the most widely employed collaborative filtering techniques due to its effectiveness and efficiency in dealing with very large user-item rating matrices. One of the principal disadvantages and challenges of the collaborative filtering type algorithms is content awareness, namely, they use only people’s behavior to produce recommendations and are not aware of the predicted content’s metadata. In this work, we study and compare two ways of incorporating this type of content information directly into the matrix factorization approach. We extend the baseline optimization problem by two techniques. The first one penalizes item and user feature vectors with some small amounts pushing them towards each other in the latent space, and the second one makes two item and user specific latent feature vectors as similar as possible if the two items and users have similar tagging history. The results of the experiments, on the benchmark data sets, show that the proposed model has a better performance compared to some other methods.
