Affiliations: [a] School of Computer Science, Hubei University of Technology, Wuhan, China
| [b] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
Abstract: Recommender systems are widely used to provide users with items they may be interested in without explicitly searching. However, they suffer from low accuracy and scalability problems. Although existing clustering techniques have been incorporated to solve these inherent problems, most of them fail to achieve further improvement in recommendation accuracy because of ignoring the correlations between items and the different effects of item attributes on recommendation results. In this article, we propose a novel recommendation algorithm to alleviate these issues to a large extent. First of all, users and items are clustered into multiple cluster subsets based on user-item rating matrix and item attribute deriving from domain experts, respectively. Then we use a selection method relying on item attribute to mine candidate items and only their predictions will be calculated in the next step, which can save the computation time greatly. Furthermore, by weighting the predictions with TF-IDF (Term Frequency-Inverse Document Frequency) weights, the top-N recommendations are generated to the target user for return. Finally, comparative experiments on two real datasets demonstrate that this algorithm provides superior recommendation accuracy in terms of MAE (Mean Absolute Error) and RMSE (Root Mean Square Error).
