A dynamic trust based two-layer neighbor selection scheme towards online recommender systems

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Elsevier B.V.


Collaborative filtering has become one of the most widely used methods for providing recommendations in various online environments. Its recommendation accuracy highly relies on the selection of appropriate neighbors for the target user/item. However, existing neighbor selection schemes have some inevitable inadequacies, such as neglecting users’ capability of providing trustworthy recommendations, and ignoring users’ preference changes. Such inadequacies may lead to drop of the recommendation accuracy, especially when recommender systems are facing the data sparseness issue caused by the dramatic increase of users and items. To improve the recommendation accuracy, we propose a novel two-layer neighbor selection scheme that takes users’ capability and trustworthiness into account. In particular, the proposed scheme consists of two modules: (1) capability module that selects the first layer neighbors based on their capability of providing recommendations and (2) a trust module that further identifies the second layer neighbors based on their dynamic trustworthiness on recommendations. The performance of the proposed scheme is validated through experiments on real user datasets. Compared to three existing neighbor selection schemes, the proposed scheme consistently achieves the highest recommendation accuracy across data sets with different degrees of sparseness.