Trust Networks are a specific kind of social network where edges in the network have positive and negative signs connoting friendship/trust and antagonism/distrust respectively. While the last few years have seen a rich body of work on generative models for social networks, there hasn’t been much work on understanding in what ways trust networks differ from social networks and to create generat...

The aim of trust prediction is to infer trust values for pairs of users when the relationship between them is unknown. The unprecedented growth in the amount of online interactions on e-commerce websites has made the problem of predicting user trust relationships critically important, yet sparsity in the amount of known (labeled) relationships poses a significant challenge to the usage of machi...

Collaborative filtering recommendation systems are facing the data sparsity problem associated with interaction data, and social recommendations introduce user information to alleviate this problem. Existing methods cannot express interest influence deeply, which limits performance of system. To address problem, in paper we propose a graph neural network algorithm integrating multi-head attenti...

Recommender system is emerging as a powerful and popular tool for online information relevant to a given user. The traditional recommendation system suffers from the cold start problem and the data sparsity problem. Many methods have been proposed to solve these problems, but few can achieve satisfactory efficiency. In this paper, we present a method which combines the trust diffusion (DiffTrus...

A mixture of positive (friendly) and negative (antagonistic) relations exist among users in most social media applications. However, many such applications do not allow users to explicitly express the polarity of their interactions. As a result most research has either ignored negative links or was limited to the few domains where such relations are explicitly expressed (e.g. Epinions trust/dis...

In Web-based social networks (WBSN), social trust relationships between users indicate the similarity of their needs and opinions. Trust can be used to make recommendations on the web because trust information enables the clustering of users based on their credibility which is an aggregation of expertise and trustworthiness. In this paper, we propose a new approach to making recommendations bas...

Identifying a customer profile of interest is a challenging task for sellers. Preferences and profile features can range during the time in accordance with current trends. In this paper we investigate the application of different model-based Collaborative Filtering (CF) techniques and in particular propose a trusted approach to user-based clustering CF. We propose a Trust-aware Clustering Colla...

Online social networks continue to witness a tremendous growth both in terms of the number of registered users and their mutual interactions. In this paper, we focus on online signed social networks where positive interactions among the users signify friendship or approval, whereas negative interactions indicate antagonism or disapproval. We introduce a novel problem which we call the link labe...

Trust relationships between user pairs play a vital role in making decisions for social network users. In reality, available explicit trust relations are often extremely sparse, therefore, inferring unknown trust relations attracts increasing attention in recent years. In this paper, a new approach originating from machine learning is proposed to predict trust relationships in social networks b...

Collaborative filtering (CF) is one of the most well-known and commonly used technology for recommender systems. However, it suffers from inherent issues such as data sparsity. Many works have been done by used additional information such as user attributes, tags and social relationships to address these problems. We proposed an algorithm named OLrs (Opinion Leaders for Recommender System) base...