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The sparsity of user-to-item rating data is one of the major obstacles to achieving high rating prediction accuracy of model-based collaborative filtering (CF) recommender systems. To overcome the obstacle, researchers proposed hybrid methods for recommender systems that exploit auxiliary information together with rating data. In particular, document modeling-based hybrid methods were recently proposed...
Due to the data sparsity problem, social network information is often additionally used to improve the performance of recommender systems. While most existing works exploit social information to reduce the rating prediction error, e.g., RMSE, a few had aimed to improve the top-k ranking prediction accuracy. This paper proposes a novel top-k ranking oriented recommendation method, TRecSo, which incorporates...
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