Construction of implicit social network and recommendation between users and items via the ISR-RRM algorithm.

To alleviate the challenges posed by privacy protection, sparse data, cold-start problem and weak interpretation, in this paper, we investigate how to uncover the implicit "social relationships" for user/item pairs and create recommendation relationships between users and items in the recommendation systems with no explicit trust relationships. Firstly, according to complex network theory, a recommendation system is presented by a heterogeneous network with five various types of subnets. Secondly, a novel algorithm ISimRank-ISR is designed based on the improved SimRank similarity index, which mainly integrates common and non-common behavior information, as well as attribute information for exploring the implicit social relations, and generates two types of social subnets. Further, introducing these implicit social relations into the nonnegative matrix factorization (NMF) model, another novel algorithm INMF-RRM is proposed to reconstruct the user-item rating subnet. Then, the integration of the above two algorithms derives an algorithm ISR-RRM to create recommendation relationships for the recommendation task. Finally, numerical experiments on two Movielens datasets are conducted to verify the feasibility and effectiveness of the three new algorithms mentioned above, and we come to the following conclusions. (1) The ISimRank-ISR algorithm provides an in-depth exploration of the implicit social relationships for user/item pairs. (2) The INMF-RRM algorithm has convergence properties. (3) To perform better and obtain more accurate recommendations, with the ISR-RRM algorithm, there are optimal parameters achieved. (4) Compared with the other seven benchmark algorithms, the ISR-RRM algorithm shows excellent performance, and its RMSE and MAE are reduced by a maximum of 14.75% and 8.05%, respectively. Additionally, it is more effective in alleviating the cold-start problem. [ABSTRACT FROM AUTHOR]