Biblio

The rapid development of the Internet and ecommerce has brought a lot of convenience to people's life. Personalized recommendation technology provides users with services that they may be interested according to users' information such as personal characteristics and historical behaviors. The research of personalized recommendation has been a hot point of data mining and social networks. In this paper, we focus on resolving the problem of data sparsity based on users' rating data and social network information, introduce a set of new measures for social trust and propose a novel personalized recommendation algorithm based on matrix factorization combining trust relevancy. Our experiments were performed on the Dianping datasets. The results show that our algorithm outperforms traditional approaches in terms of accuracy and stability.

Collaborative filtering (CF) recommender system has been widely used for its well performing in personalized recommendation, but CF recommender system is vulnerable to shilling attacks in which shilling attack profiles are injected into the system by attackers to affect recommendations. Design robust recommender system and propose attack detection methods are the main research direction to handle shilling attacks, among which unsupervised PCA is particularly effective in experiment, but if we have no information about the number of shilling attack profiles, the unsupervised PCA will be suffered. In this paper, a new unsupervised detection method which combine PCA and data complexity has been proposed to detect shilling attacks. In the proposed method, PCA is used to select suspected attack profiles, and data complexity is used to pick out the authentic profiles from suspected attack profiles. Compared with the traditional PCA, the proposed method could perform well and there is no need to determine the number of shilling attack profiles in advance.

With the repaid growth of social tagging users, it becomes very important for social tagging systems how the required resources are recommended to users rapidly and accurately. Firstly, the architecture of an agent-based intelligent social tagging system is constructed using agent technology. Secondly, the design and implementation of user interest mining, personalized recommendation and common preference group recommendation are presented. Finally, a self-adaptive recommendation strategy for social tagging and its implementation are proposed based on the analysis to the shortcoming of the personalized recommendation strategy and the common preference group recommendation strategy. The self-adaptive recommendation strategy achieves equilibrium selection between efficiency and accuracy, so that it solves the contradiction between efficiency and accuracy in the personalized recommendation model and the common preference recommendation model.