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DAC '96 Proceedings of the 33rd annual Design Automation Conference
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
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Evaluating and Optimizing Thread Pool Strategies for Real-Time CORBA
OM '01 Proceedings of the 2001 ACM SIGPLAN workshop on Optimization of middleware and distributed systems
MovieLens unplugged: experiences with an occasionally connected recommender system
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Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
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The Journal of Machine Learning Research
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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ALS'00 Proceedings of the 4th annual Linux Showcase & Conference - Volume 4
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HPCA '07 Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture
TagiCoFi: tag informed collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Collaborative filtering with temporal dynamics
Communications of the ACM
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
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ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
IMIS '11 Proceedings of the 2011 Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing
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Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
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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.