A Comprehensive Social Matrix Factorization with Social Regularization for Recommendation Based on Implicit Similarity by Fusing Trust Relationships and Social Tags

Abstract

Social relationships play an important role in improving the quality of recommender systems (RSs). A large number of experimental results show that social relationship-based recommendation methods alleviate the problems of data sparseness and cold start in RSs to some extent. However, since the social relationships between users are extremely sparse and complex, and it is difficult to obtain accurately user preference model, thus the performance of the recommendation system is affected by the existing social recommendation methods. In order to accurately model social relationships and improve recommendation quality, we use explicit social relationships such as user-item ratings, trust relationships and implicit social relationships such as social tags to mine potential interest preferences of users and propose an improved social recommendation method integrating trust relationship and social tags. The method map user features and item features to the shared feature space by using the above social relationship, respectively, and obtains user similarity and item similarity through potential feature vectors of users and items, and continuously trains them to obtain accurate similarity relationship to improve the recommendation performance. Experimental results demonstrate that our proposed approach achieves superior performance to the other social recommendation approaches.

A Survey on Recommendation Methods Based on Social Relationships

“…In recent years, domestic and foreign researchers have conducted extensive research on RSs based on social networks and proposed many effective recommendation algorithms. Although these recommendation algorithms use different technologies, the recommendation framework models are all based on the structural characteristics of social networks, the popularity of items in social groups, and the impact of social relationship information between users on recommendation quality [6,8,23,40]. The basic framework of a social network RS is shown in Figure 2, which includes four steps [9,10,32,41]: Social network analysis is a very popular social science research method that studies social phenomena and structures from the perspective of social relationships.…”

“…MF has a strong generalization ability, but it requires a large amount of computation and is not suitable for dealing with large-scale sparse matrices. In 2007, a trust-aware CF recommendation algorithm was proposed, which is a memory-based CF recommendation algorithm that integrates the user's trust propagation mechanism into the CF recommendation algorithm [23]. In 2009, a probability factor factorization model was proposed, which fuses user interests with the preferences of trusted friends.…”

“…Due to the ability of social relationships to alleviate issues of data sparsity and cold start, recommendation based on social relationships dominates. According to the adopted technology, RSs can be divided into following categories [10,11,23].…”

“…According to the formal definition of traditional RSs, combined with the influencing factors of social relationships, RSs based on social networks can be formally described as follows [48]: Definition 1. An RS based on social networks [23,49]. An RS based on social networks can be represented by a five tuple, i.e., RS = (U, I, S, ϕ, R), where U, I, and S denotes the user set, item set, and the user social relationship matrix, respectively.…”

PEVGraphRec: a PEV method-based graph neural networks for social recommendations

“…Previous endeavour [5] using the techniques of Matrix Factorization (MF) [6] learns low-dimensional latent features of each user and item. Most MF-based Collaborative Filtering (CF) methods combine the potential features of users and items linearly, resulting in limited performance when handling highly complicated real-world data [7].…”