关键词: 推荐系统, 协同过滤, 四部图, 协同过滤标签

Abstract: A recommender system often collects information about user profiles, item attributes and explicit ratings of users to items, which are further used to make predictions about unknown ratings. This paper constructs a quadripartite graph about the information and acquires ten algorithms from different parts of the graph. The first two algorithms are the classical user- and item-based collaborative filtering and only take into account the rating information. Four more algorithms take user or item as center and use relevant tags to compute user or item similarity. To extend the previous four algorithms, four more algorithms take into account the user-item relationship along with tag information. This paper compares the time complexity of different algorithms on two MovieLens data sets, and uses MAE (mean absolute error) and RMSE (root-mean-square error) metrics to evaluate the performance of different    algorithms. The experimental results demonstrate that the similarity of items is more reliable than that of users, and item tags are more useful than user tags. Besides, some simple linear integrations of different information are capable of enhancing recommendation performance.

Key words: recommender system, collaborative filtering, quadripartite graph, collaborative filtering tag