Cascaded Trust Network-based Block-Incremental Recommendation Strategy

摘要

Accurate recommendation can effectively bridge sellers and buyers. Because the computation complexity and storage complexity of static data-oriented recommendation algorithms are very high, researchers have recently explored streaming recommendation systems. However, streaming recommendation wastes large quantities of computation resources in quick response and is not suitable for seasonable-dependent situations. Therefore, this paper presents a block incremental recommendation strategy. First, a cascaded trust network construction method is presented, which is realized by using a distrust relationship to purify and predict users' trust relationships. Then, the social regularization is improved by comprehensively considering the cascaded trust relationship, the behavior bias of users and items. Finally, a block-incremental recommendation algorithm called ITDBMF is proposed, which uses the Ebbinghaus forgetting function to decay incremental rating blocks and simultaneously considers incremental social relationships. Experimental results show that the incremental recommendation strategy given in this paper can not only outperform benchmark algorithms in prediction accuracy, but also save storage of remote data and matrix factorization time.