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Collaborative filtering recommender systems are essentially information systems which are capable of combining the judgment of a large group of people to make personalized recommendations and thereby alleviate the so-called information overload problem. However,collaborative filtering recommender systems are generally vulnerable to shilling attacks. Attackers can inject carefully chosen profiles into...
Recommender systems are widely used in online business to satisfy user personalization demands. The most successful technique of such systems is collaborative filtering, which utilizes users' known preference to generate predictions of the unknown preferences. A key challenge for collaborative filtering recommender systems is providing high quality recommendations to new users that have not enough...
Personalized Travel Itinerary Recommendation Service (PTIRS) is a hot research problem in E-travel information management currently. To improve the problem, in this paper a PTIRS method based on collaborative Altering and IEC is proposed. Firstly the scale of travel itinerary sample set is reduced by collaborative Altering; secondly an IEC algorithm called IMAGA is used to accelerate convergence speed...
Currently collaborative filtering is the most successful and widely used recommendation technology in e-commerce recommender systems. The idea behind this technology is that it may be of benefit to user' s search for products by examining the behavior of other users who share the same or similar interests with her/him. In this paper, an e-commerce recommender system using collaborative filtering,...
Collaborative filtering is the most successful and widely used recommendation algorithm in E-commerce recommender systems currently. However, it faces severe challenge of cold-start problem. To solve the new item problem in cold-start, a cold-start recommendation method based on dynamic browsing tree model is proposed. Firstly, user browsing records are transformed to dynamic browsing tree (DBT) based...
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