摘要: Collaborative filtering is the most popular approach to build recommender systems and has been successfully employed in many applications. However, it cannot make recommendations for so-called cold start users that have rated only a very small number of items. In addition, these methods do not know how confident they are their recommendations. Trust-based recommendation assume additional knowledge trust network among can better deal with users, since need be simply connected network. On other hand, sparsity user item ratings forces trust-based consider indirect neighbors weakly trusted, which may decrease its precision. order find good trade-off, we propose random walk model combining collaborative recommendation. The allows us define measure confidence We performed an evaluation on Epinions dataset compared our existing methods.
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brettb.net 参考文章(17)
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