NDLI: Semi-sparse algorithm based on multi-layer optimization for recommendation system

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Semi-sparse algorithm based on multi-layer optimization for recommendation system

Content Provider	ACM Digital Library
Author	Guo, Minyi Guan, Hu Li, Huakang
Abstract	Collaborative filter (CF) is the most successful technique in recommender system, which makes personalize recommendations during online interaction. We propose a new Semi-sparse algorithm based on multi-layer optimization to speed up the basic Pearson Correlation Coefficient of CF. Semi-sparse algorithm spares out over-reduplicate accessing and judgement on selected sparse vector to accelerate the batch of similarity-comparisons in one thread. We propose a reduce-vector in thread-pool to restrict the lock using on critical resources in parallelize implementation. Thread-pool is wrapped with Pthreads on multi-core node to make semi-sparse parallelization more easily. A shared zip file is read to cut down messages with Message Passing Interface package. The performance of proposed semi-sparse with multi-layer framework achieved a brilliant speedup in the evaluation of Netflix, MovieLens and MovieLen1600.
Starting Page	148
Ending Page	155
Page Count	8
File Format	PDF
ISBN	9781450312110
DOI	10.1145/2141702.2141719
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-02-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Semi-sparse algorithm Message passing interface Thread pool Reduce vector Recommendation system
Content Type	Text
Resource Type	Article

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