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Collaborative filtering is one of the widely used technologies in the e-commerce recommender systems. It can predict the interests of a user based on the rating information of many other users. But the traditional collaborative filtering recommendation algorithm has the problems such as lower recommendation precision and weaker robustness. To solve these problems, in this paper we present a robust collaborative filtering recommendation algorithm based on multidimensional trust model. Firstly, according to the rating information of users, a multidimensional trust model is proposed. It measures the credibility of user’s ratings from the following three aspects: the reliability of item recommendation, the rating similarity and the user’s trustworthiness. Secondly, the computational model of trust and the traditional collaborative filtering approach are combined to select the reliable neighbor set and generate recommendation for the target user. Finally, the performances of the novel algorithm with others are compared from both sides of recommendation precision and robustness using MovieLens dataset. Compared with the existing algorithms, the proposed algorithm not only improves the quality of neighbor selection and the recommendation precision, but also has better robustness

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Personalized support for users becomes even more important, when service access takes place in open and dynamic service-oriented environment. This paper shows how to realize personalized service support in cross-system/service environments based on ontology and Web service technologies. First, we introduce the related approaches for supporting cross-system personalization and give their insufficiency respectively. Aimed at the problems we propose a Web service based architecture for cross-system personalization. The loosely coupled structure of Web service can easily integrate personalization service from various information systems and provide seamless access to the users. In order to reuse user models we also present an ontology based user model to support cross-system personalization. Compared with the existing approaches, our approach can effectively support the various existing personalized systems and user models, and the realization of crosssystem personalization is more simply and efficiently.

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The existing supervised approaches suffer from low precision when detecting profile injection attacks. To solve this problem, the authors propose an ensemble detection model by introducing back propogation (BP) neural network and ensemble learning technique. Firstly, through combination of various attack types, they create base training sets which include various samples of attack profiles and have great diversities with each other. Secondly, they use the created base training sets to train BP neural networks to generate diverse base classifiers. Finally, they select parts of the base classifiers which have the highest precision on the validation dataset and integrate them using voting strategy. Uncorrelated misclassifications generated by each base classifier can be successfully corrected by the ensemble learning. The experimental results on two different scale of the real datasets MovieLens and Netflix show that the proposed model can effectively improve the precision under the condition of holding a high recall.