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Content Provider | IEEE Xplore Digital Library |
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Author | Fan Lu Li Hong Li Changfeng |
Copyright Year | 2015 |
Description | Author affiliation: Acad. of Inf. Eng., Central South Univ., Changsha, China (Fan Lu; Li Hong; Li Changfeng) |
Abstract | In the background of big data era, after analyzing the traditional collaborative filtering algorithm, this paper proposes improved item-based collaborative filtering algorithm using “hotweight” as the weight, which is aimed at improving the accuracy of the algorithm and overcoming the defects such as rarefaction and cold-starting. We distribute the algorithm with MapReduce framework, and apply it to the distributed cluster platform Hadoop. This paper adopts real data set to run the algorithm and the experiment's result expresses that the improved algorithm can run efficiently on the large amounts of data with the better accuracy, and at the same time, can overcome the cold-starting drawback successfully. |
Starting Page | 9078 |
Ending Page | 9083 |
File Size | 471374 |
Page Count | 6 |
File Format | |
ISBN | 9789881563897 |
ISSN | 19341768 |
DOI | 10.1109/ChiCC.2015.7261076 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2015-07-28 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Technical Committee on Control Theory, Chinese Association of Automation |
Subject Keyword | Algorithm design and analysis Accuracy Collaboration Clustering algorithms hotweight Hadoop Filtering algorithms Big data collaborative filtering algorithm Sparse matrices MapReduce |
Content Type | Text |
Resource Type | Article |
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