A Factored Similarity Model with Trust and Social Influence for Top-N Recommendation

Many trust-aware recommendation systems have emerged to overcome the problem of data sparsity, which bottlenecks the performance of traditional Collaborative Filtering (CF) recommendation algorithms. However, these systems most rely on the binary social network information, failing to consider the variety of trust values between users. To make up for the defect, this paper designs a novel Top-N recommendation model based on trust and social influence, in which the most influential users are determined by the Improved Structural Holes (ISH) method. Specifically, the features in Matrix Factorization (MF) were configured by deep learning rather than random initialization, which has a negative impact on prediction of item rating. In addition, a trust measurement model was created to quantify the strength of implicit trust. The experimental result shows that our approach can solve the adverse impacts of data sparsity and enhance the recommendation accuracy

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

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