Abstract
In order to alleviate the pressure of information overload and enhance consumer satisfaction, personalization recommendation has become increasingly popular in recent years. As a result, various approaches for recommendation have been proposed in the past few years. However, traditional recommendation methods are still troubled with typical issues such as cold start, sparsity, and low accuracy. To address these problems, this paper proposed an improved recommendation method based on trust relationships in social networks to improve the performance of recommendations. In particular, we define trust relationship afresh and consider several representative factors in the formalization of trust relationships. To verify the proposed approach comprehensively, this paper conducted experiments in three ways. The experimental results show that our proposed approach leads to a substantial increase in prediction accuracy and is very helpful in dealing with cold start and sparsity.
Highlights
Social networks have played an important role on the Internet, which focuses on interactions and relationships between people
There are many studies on recommendations in social networks, most of them still suffer from typical problems such as cold start, sparsity, and unsatisfactory precision
We investigate here the problem of how trust relationships can benefit recommendations in a social network
Summary
Social networks have played an important role on the Internet, which focuses on interactions and relationships between people. The Internet can be regarded as a new platform, on which people pursue an increasing amount of activities that they have usually only done in the real world [1] This platform is troubled by information overload, which makes it difficult to find personalized information from the vast amounts of information on the Internet. An increasing amount of studies have been conducted on recommendations based on trust [3,4,5] These methods, tend to be vague in defining trust relationships, and they rarely consider indirect trust. To solve these challenges, we investigate here the problem of how trust relationships can benefit recommendations in a social network.
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