NDLI: Collaborative filtering recommendation algorithm based on shift of users' preferences

Content Provider	IEEE Xplore Digital Library
Author	Min Xiao Bingjie Yan
Copyright Year	2011
Description	Author affiliation: Department of Computer Science, Wuhan University of Technology, China (Min Xiao) \|\| Department of Computer and Information, Engineering Wuhan Bioengineering Institute, China (Bingjie Yan)
Abstract	The weight of all users' score is the same in traditional collaborative filtering recommendation algorithm, and it doesn't consider the shift of users' preferences with time, so recommendation quality is poor. In order to avoid the problems above, a novel collaborative filtering algorithm based on shift of users' preferences is presented: The method adjusts the weight of users' score according to time, improves users' similarity with a gradual forgetting function, and considers the impacts on similarity between users brought by the shift of users' preferences, then clusters users in terms of their features, reduces chosen space of the nearest neighbors. The experiment result shows that this method has better recall and precision than traditional collaborative filtering recommendation algorithm, and it can effectively improve recommendation quality.
Starting Page	520
Ending Page	523
File Size	82802
Page Count	4
File Format	PDF
ISBN	9781612841083
e-ISBN	9781612841090
DOI	10.1109/ICBMEI.2011.5920508
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Computers Accuracy Filtering Similarity Collaborative filtering Collaboration Clustering algorithms Filtering algorithms Clustering Shift of users' preferences Users' features
Content Type	Text
Resource Type	Article

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