Relational Collaborative Topic Regression for Recommender Systems
Abstract— Relational Collaborative Topic Regression for Recommender Systems. Due to its successful application in recommender systems, collaborative ﬁltering (CF) has become a hot research topic in data mining and information retrieval. In traditional CF methods, only the feedback matrix, which contains either explicit feedback < Final Year Projects 2016 > also called ratings > or implicit feedback on the items given by users, is used for training and prediction. Typically, the feedback matrix is sparse, which means that most users interact with few items. Due to this sparsity problem, traditional CF with only feedback information will suffer from unsatisfactory performance. Recently, many researchers have proposed to utilize auxiliary information, such as item content (attributes), to alleviate the data sparsity problem in CF.