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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
Proceedings of the 10th international conference on World Wide Web
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
nfsp: A Distributed NFS Server for Clusters of Workstations
IPDPS '02 Proceedings of the 16th International Symposium on Parallel and Distributed Processing
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Item-based top-N recommendation algorithms
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ALS'00 Proceedings of the 4th annual Linux Showcase & Conference - Volume 4
Evaluating MapReduce for Multi-core and Multiprocessor Systems
HPCA '07 Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture
TagiCoFi: tag informed collaborative filtering
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Collaborative filtering with temporal dynamics
Communications of the ACM
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
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CIT '10 Proceedings of the 2010 10th IEEE International Conference on Computer and Information Technology
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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
Concurrent programming constructs for parallel MPI applications
The Journal of Supercomputing
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Similarity among vectors is basic knowledge required to carry out recommendation and classification in recommender systems, which support personalized recommendation during online interactions. In this paper, we propose a Semi-sparse Algorithm based on Multi-layer Optimization to speed up the Pearson Correlation Coefficient, which is conventionally used in obtaining similarity among sparse vectors. In accelerating the batch of similarity-comparisons within one thread, the semi-sparse algorithm spares out over-reduplicated accesses and judgements on the selected sparse vector by making this vector dense locally. Moreover, a reduce-vector is proposed to restrict using locks on critical resources in the thread-pool, which is wrapped with Pthreads on a multi-core node to improve parallelism. Furthermore, among processes in our framework, a shared zip file is read to cut down messages within the Message Passing Interface package. Evaluation shows that the optimized multi-layer framework achieves a brilliant speedup on three benchmarks, Netflix, MovieLens and MovieLen1600.