Abstract
The diffusion-based algorithm is a promising member of the family of recommendation algorithms. It makes recommendations through the diffusion process on user-object bipartite graphs. However, a user’s taste is often influenced by his/her trusted friends in social networks. In this paper, we propose a new trust-based diffusion on tripartite graphs, which integrates explicit trust relations and implicit trust relations into the diffusion process. Explicit trust relations are obtained from the social networks while implicit trust relations are inferred from implicit feedback. The experimental results indicate that our proposed method has a remarkable improvement in accuracy, and even only implicit trust relations employed, that is, diffusion on user-object bipartite graphs, the recommendation accuracy is still enhanced. We further present a general framework of applying implicit trust relations and explicit trust relations into basic network-based diffusion method, which is more general and flexible, and specific parameters can be selected to meet actual requirements for different datasets and real-world online platforms.
Highlights
It becomes extremely hard to gain what people want from tons of data in modern society
In this paper, we explored how to integrate trust relations into network-based diffusion process to improve the accuracy of recommendation and proposed trust-based mass diffusion algorithm (TrustMD) algorithm
User trust relations are classified into two categories: implicit trust relations and explicit trust relations, and both of them are integrated into the standard mass diffusion process (MD)
Summary
It becomes extremely hard to gain what people want from tons of data in modern society. Information overload has become a serious problem. Recommender systems have been widely employed, which provide an effective way to filter information. The task of recommender systems is to predict users’ preferences based on past historical behaviors, attributes of objects, spatio-temporal context, and so on [1]. Recommender systems are applied in a variety of situations such as e-commerce platforms, news websites, social network service, research article websites, location-related service, which benefit both the service providers and the users [2]–[5]. Various recommendation algorithms have been proposed and one of the most successful is collaborative filtering [6]. Some scholars model users and objects in recommender systems as userobject bipartite graphs, and inspired by physics dynamics
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