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In traditional e-commerce websites, social tags are used in product classification only, and not applied in the domain of personalized recommendation technology. In this paper, we propose a personalized recommendation model based on social tags. We build a user interest model for products by reflecting user interest and product features directly through social tags, and optimize the interest model...
Collaborative filtering has been very successful in both research and applications. Current collaborative filtering based on clustering compute the whole set of items during the process of clustering or selecting nearest-neighbors, because the researchers believed if users have similar preferences on some of items, they will have the similar preferences on other items. But we think that users have...
Collaborative Filtering (CF) is a method behind the successful of recommendation system by attempting to predict user interested from peer's opinions and recommend similar items that match user's interested. A Challenge for collaborative filtering is data characteristics. It's always contains a lot of missing values either by gotten number of rating from user is very low or new items add to the system...
Personalized recommender systems are producing recommendations and widely used in the electronic commerce. Collaborative filtering technique has been proved to be one of the most successful techniques in recommendation systems in recent years. However, most existing collaborative filtering based recommendation systems suffered from its shortage in scalability as their calculation complexity and space...
Automated collaborative filtering has become a popular technique for reducing information overload. We have developed a new method for recommending items using multiple agents. The agents were established by employing the fuzzy C-means clustering technique. We employ these agents collaborating each other to get recommendation for users. The results were evaluated by using MovieLens movie's rating...
This paper proposes a novel algorithm named item-based clustering recommendation algorithm (IBCRA) for reducing the poor recommendation quality due to the data sparsity and high dimension. Specifically, on the basis of high-dimensions data clustering algorithms, the IBCRA uses the rating data sparse difference and item categories in the rating dataset to construct a measuring formula for calculating...
Most e-mail users have encountered with spam problems, which have been addressed as a text classification or categorization problem. In this paper, we propose a novel spam detection method that uses ensemble of classifiers based on clustering and selection techniques. There is diversity in genre of e-mail's content and this method can find different topics in emails by clustering. It first computes...
In this paper we present the recommender systems that use the k-means clustering method in order to solve the problems associated with neighbor selection. The first method is to solve the problem in which customers belong to different clusters due to the distance-based characteristics despite the fact that they are similar customers, by properly converting data before performing clustering. The second...
Personalized recommendation systems can help people to find interesting things and they are widely used in our life. Poor quality is one major challenge in collaborative filtering recommender systems. Sparsity of source data set is the major reason causing the poor quality. Aiming at the problem of data sparsity for collaborative filtering, a novel rough set and fuzzy clustering based collaborative...
Collaborative filtering technique has been proved to be one of the most successful techniques in recommendation systems in recent years. However, most existing collaborative filtering based recommendation systems suffered from its shortage in scalability as their calculation complexity increased quickly both in time and space when the record in user database increases. So, a new collaborative filtering...
This paper, which is work in progress, describes a new cluster based approach for Image restoration applications. The existing and the past solutions to Image restoration problem has always concentrated on single uniprocessor machines. This has become a major hurdle for large images as well as motion picture which requires large portions to be rendered at a faster rate. We propose a cluster based...
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