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The successful design of collaborative filtering system for recommending depends hardly on finding the nearest neighbors. In this paper, we provide a new collaborative filtering method based on concept lattices to generate more precise recommendation. Firstly, we analyze the log files and construct a formal context which is then used to build a concept lattice. Based on the concept lattice, we propose...
In recent years, extensive researches have been conducted to develop approaches to answer two major challenges for collaborative filtering problems, namely sparsity and scalability. In this paper, we propose a novel collaborative filtering recommendation approach to alleviate these challenges. Our approach firstly converts the user-item ratings matrix to user-class matrix, and hence increases greatly...
Collaborative filtering is the most widely used and successful technology for building recommender systems. However it faces challenges of scalability and recommendation accuracy. Collaborative filtering can be divided into memory based and model based. The former is more accurate while the latter performs better in scalability. This paper proposes a hybrid user model. The recommender system based...
This paper applied Multi-Agent to E-commerce personalized Recommender System, and designed E-commerce personalized Recommender System based on Multi-Agent, namely, MAPRS. Off-line recommendation and on-line hybrid recommendation are used to construct the core recommender model under the intelligent control. The paper presents the function and design ideas of various components of the system.
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