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The rapid development of e-commerce has greatly changed the lifestyle of people. Nowadays, people are used to buying various kinds of things online, and recommender systems become more and more necessary since users are overwhelmed by a large amount of information. However, that a user's consuming behavior would change with his life stage has not been taken into consideration in most existing recommender...
Identifying users' similarity is a very important researching point because its result can be applied to many application systems. In social networks, the user circles are built not only based on their relationships in real-life, but also on common interests. Some existing approaches cannot fully capture users' similarity from the perspective of their common interests, while some other approaches...
Collaborative filtering (CF) is one of the major approaches to building recommender systems. Traditional batch-trained algorithms for CF suffer from some drawbacks, and online learning algorithms for CF, is a promising tool for attacking the large-scale dynamic problems. However, the low time complexity of online algorithm often be accompanied by low convergence rate, and the convergence rate of current...
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