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In order to overcome problems of scoring subjectively, this paper applies CF to recommendation systems of Mobile value-added service, presents the scoring way in value-added service, transfer the users' behavior to the extent of agreement. Meanwhile, using user contact degree to calculate the product rate which users don't give rate in the real world, so as to solve the sparsity problem of CF. At...
As we all know, the World Wide Web is becoming a large source of images. Thus, it is manifest that the demand for image recommendation systems is increasing rapidly. There are several image recommendation systems for both commercial and academic areas, which deals with the user preference being fixed. However, since the Images preferred by a user may change depending on the contexts, the conventional...
With the expansion of digital networks and TV devices and the rapid increase of the number of channels, people are exposed to an information overload, due to the presence of several hundreds of alternative programs to watch. In this context, personalization is achieved with the employment of algorithms and data collection schemes that predict and recommend to television viewers content that match...
Recommender systems, in the collaborative filtering variation, are popular tools used to drive users out of information clutter, by letting them select ??interesting?? items based on the preferences of similarly minded users. In such a system as more users come in to evaluate items (be they information pieces, products or otherwise), a network of users starts to be formed. In this paper we are interested...
Recommender systems are becoming increasingly popular with the evolution of the Internet, and collaborative filtering that using explicit ratings on items from users is the most successful technology for building recommendation systems. But traditional collaborative filtering algorithm is not suitable for itempsilas multiple content and multiple level recommendations. So, a new concept hierarchy methodology...
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