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Research on Personalized Recommender System Based on Collaborative Filtering Algorithm

Author: ChengShuYu
Tutor: WangHao；WangWeiWei
School: Hefei University of Technology
Course: Computer technology
Keywords: Individuation Recommended system Collaborative filtering WEB mining User clustering
CLC: TP393.09
Type: Master's thesis
Year: 2010
Downloads: 300
Quote: 1
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Abstract


Information overload and information lost people has become a constraining bottleneck of the efficient use of information on the Internet . Information overload is one of the great wealth of the negative impact of the Information Age is rapidly increasing , with the rapid development of Internet applications , online information , so that the face of too much information is difficult to timely digestion and absorption . Information lost is due to the wide distribution of information resources on the Internet , users do not know exactly how to express the needs of online information , do not know how to accurately and efficiently find the information they are interested in . At present , most of the search engines due to the lack of initiative , without considering the user's interest preferences, can not effectively solve the problem of information overload and information lost . The recommended system is a technology derived on the basis of information filtering technology . Information filtering technology can solve the problem of \Information filtering technology is the basis of information resources personalized recommendation . However, with the constant expansion of Internet Information , recommendation system is also facing a series of problems , this paper combined with the existing site resources personalized recommendation system algorithm . This paper first introduces collaborative filtering of Web mining technology and personalized recommendation system , then introduces the principle and application of collaborative filtering algorithm , and analyze them , and pointed out the problems of the algorithm is proposed based on Web logs and clustering analysis algorithm that the system is offline , log server WEB the user clustering method will be divided into the same cluster users with similar interests degrees , while the system is online to find the nearest neighbor to a target user recommended only need to find its neighbors and its degree of interest in the most similar clustering , and then recommended to the target user is most likely interested in commodities based on the neighbor's interest to improve the efficiency of online data processing , the algorithm is applied to the the studied site , so that the algorithm can effectively improve the quality of recommendation system and recommended efficiency .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Computer network > General issues > The application of computer network
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