Abstract
As one of the most important techniques in recommender systems, collaborative filtering (CF) generates the recommendations or predictions based on the observed preferences. Most traditional recommender systems fail to discover the latent associations between the same or similar items with different names, which is called synonymy problem. With the rapid increasing number of users and items, the user-item rating data is extremely sparse. Based on the limited number of user ratings, we cannot capture enough information from the user history using the traditional CF techniques, which could reduce the effectiveness of the recommender systems.
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