# Improved Recommendations Based on Trust Relationships in Social Networks
## 2 Related Work  
Trust is a new research hotspot that has been widely discussed by many scholars in recent years. In [4], the authors proposed a social network-based service recommendation method with trust enhancement known as the Relevant Trust Walker; however, this method neglects the fact that trust relationships between users are time-varying. [PERSON] and [PERSON] presented a trust model for P2P networks in [5], but due to the lack of flexibility, the model lost some parts as the points of peers changed. To improve the accuracy of top-k ranking predictions, a method based on an objective function for recommendation is presented in [6], in which users are divided into two classes of trustees and trusters. The experimental results show that the method has a satisfactory accuracy of recommendation in top-k ranking, but a partial recall due to time complexity. Based on the technology of Tensor Factorization, [PERSON] and [PERSON] presented a trust model that represented the extra information as a tensor in [7]. The model can integrate multidimensional contextual information to provide satisfactory recommendations, but it lacks flexibility. In [8], the authors defined trust and reputation, and introduced corresponding computation methods. Specially, trust is divided into two subclasses--reliability trust and decision trust--and an agent-oriented recommendation system based on trust is also presented. The evaluation results show advantages of trust in movie recommendations. To deal with the issues of cold start and sparsity, [PERSON] and [PERSON] proposed a random walk model in [9] that can combine the trust-based and the collaborative filtering method to generate recommendations. The random allows users to define and measure the confidence of a recommendation; however, the trust concept they considered in the paper is context-independent. In [10], two novel methods that exploit trust networks to improve the results of top-N recommendations were proposed. The first method employs a random walk on a trust network by calculating the similarity of users in its termination condition. The second method combines collaborative filtering and a trust-based approach. To alleviate the cold-user problem, the authors used a special API (Application Programming Interface) to select the most valuable node in a certain venue to calculate the probability of similar favorite categories in [11]. The approach can achieve the established goal but is still troubled by the sparsity problem. In [12], the authors tried to handle the cold start problem by employing implicit social information to provide personalized news recommendations in a news reading community. They proposed a novel framework that treats implicit reader feedbacks as assistant information for recommendations. [PERSON] and [PERSON] introduced social preference ontologies to solve the problems of cold start and sparsity in [13]. Evaluation results show that the method performs well but domain concepts need to be identified to support preference ontologies. [PERSON] and [PERSON] analyzed the robustness of some typical recommendation approaches based on distributed data in [14]. They summarized reasons why these methods are vulnerable to attacks by malicious users. In order to restrain malicious recommendations and fraud service actions, [PERSON] and [PERSON] proposed a dynamic recommendation trust-evaluated model based on bidding in an E-Commerce environment in [15]. In this method, the value of trust is essentially computed based on the number of happy and unhappy interactions, but the definitions of the two statuses are not yet clear. In [16], [PERSON] and [PERSON] developed an implicit trust filtering recommendation approach and an enhanced user-based collaborative filtering recommendation approach to select a trustworthy business partner to perform reliable business transactions. Since the paper computes the trust value based on mean ratings by users, its reliability and sensitivity is not high.