NDLI: Removing redundancy and inconsistency in memory-based collaborative filtering

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Removing redundancy and inconsistency in memory-based collaborative filtering

Content Provider	ACM Digital Library
Author	Yu, Kai Tresp, Volker Schwaighofer, Anton Xu, Xiaowei Kriegel, Hans-Peter
Abstract	The application range of memory-based collaborative filtering (CF) is limited due to CF's high memory consumption and long runtime. The approach presented in this paper removes redundant and inconsistent instances (users) from the data. This paper aims to distinguish informative instances (users) from large raw user preference database and thus alleviate the memory and runtime cost of the widely used memory-based collaborative filtering (CF) algorithm. Our work shows that a satisfactory accuracy can be achieved by using only a small portion of the original data set, thereby alleviating the storage and runtime cost of the CF algorithm. In our approach, we consider instance selection as the problem of selecting informative data that increase the We begin by discussing the instance selection problem in a general sense that is to increase a posteriori probability of the optimal model by selecting informative data. We evaluate the empirical performance of our approach PF on two real-world data sets and attain very promisingpositive experimental results. The dData size and the prediction time are significantly reduced, while the prediction accuracy is on a par with almost the same as the results achieved by using the complete database.
Starting Page	52
Ending Page	59
Page Count	8
File Format	PDF
ISBN	1581134924
DOI	10.1145/584792.584804
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2002-11-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Collaborative filtering Scalability Efficiency Data selection
Content Type	Text
Resource Type	Article

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