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 ommendation 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 has crogenous network with various types of subnets. Secondly, a novel algorithm 15mRank-15 is designed based on the improved Simiank similarity index, which mainly integrates common and non-common behavior information, as well as attribute information for exploring the implicit social relations, and generates trop types of social subnets. Further, introducing these implicit social relations into the nonnegative matrix factorization QMF) model, another novel algorithm MF-RPM is proposed to reestruct the user-item rating subnet. Then, the integration of the above ne sigarithms derives an algorithm ISR-RAM to crease recommendation ro lationships for the recommendation task. Finally, numerical experiments on ve Meviclens data are con ducted to verify the feasibility and effectiveness of the three new algorithms mentioned above, and we come to the following sanclusions. (1) The SimRank-ISR algorithm provides an in-depth exploration of the implicit social relationships for user item pairs. (2) The IMF-RAM algorithm has convergence properties. (5) To perform better and obtain more accurate recommendations, with the 15R-RAM alganthm, there are optimal parameters achieved. (4) Compared with the other seven benchmark algorithms, the ISR-ARM algorithm shows excellen performance, and its RMSE and MAE are reduced by a maximum of 14,75% and 8.05%, respectively. Addin sionally, it is more effective in alleviating the cold-start problem