NDLI: Improving efficiency of recommender systems

Content Provider	IEEE Xplore Digital Library
Author	Chih-Lun Liao Yu-Chun Lin Shing-Tai Pan Shie-Jue Lee
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung, Taiwan (Chih-Lun Liao; Yu-Chun Lin; Shie-Jue Lee) \|\| Dept. of Comput. Sci. & Inf. Eng., Nat. Univ. of Kaohsiung, Kaohsiung, Taiwan (Shing-Tai Pan)
Abstract	By learning from the past behaviors of user transaction records, recommender systems can help people to nd interesting products from many other products. In a collaborative ltering based recommender system, products are regarded as features. However, there are usually quite a lot of products to be considered. A recommender system would be very inefficient if such a large number of products are processed before making any recommendations. We propose a method which applies a self-constructing clustering technique to reduce the dimensionality related to the number of products. As a result, the processing time for making recommendations is much reduced without degrading the accuracy of recommendations.
Starting Page	1
Ending Page	4
File Size	96927
Page Count	4
File Format	PDF
ISBN	9781479986392
ISSN	21593450
e-ISBN	9781479986415
DOI	10.1109/TENCON.2015.7373129
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recommender systems Collaboration Feature extraction Training Correlation Silicon Testing feature extraction Recommender system correlation graph clustering
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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