FASTCF

Author:

Zhou Shijie1,Kannan Rajgopal2,Min Yu1,Prasanna Viktor K.1

Affiliation:

1. University of Southern California, Los Angeles, CA, USA

2. US Army Research Lab, Los Angeles, CA, USA

Funder

U.S. National Science Foundation

Publisher

ACM

Reference40 articles.

1. Matrix Factorization Techniques for Recommender Systems

2. The Netflix Recommender System

3. B. Chen D. Agarwal P. Elango and R. Ramakrishnan "Latent Factor Models for Web Recommender Systems " http://www.ideal.ece.utexas. edu/seminar/LatentFactorModels.pdf B. Chen D. Agarwal P. Elango and R. Ramakrishnan "Latent Factor Models for Web Recommender Systems " http://www.ideal.ece.utexas. edu/seminar/LatentFactorModels.pdf

4. "Netflix Update: Try This at Home " http://sifter.org/~simon/journal/ 20061211.html "Netflix Update: Try This at Home " http://sifter.org/~simon/journal/ 20061211.html

5. A Survey of Collaborative Filtering Techniques

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1. An edge re-ordering based acceleration architecture for improving data locality in graph analytics applications;Microprocessors and Microsystems;2023-10

2. GGPA;Proceedings of the 20th ACM International Conference on Computing Frontiers;2023-05-09

3. PCFBCD: An Innovative Approach to Accelerating Collaborative Filtering;2022 IEEE International Symposium on Circuits and Systems (ISCAS);2022-05-28

4. GraphDEAR: An Accelerator Architecture for Exploiting Cache Locality in Graph Analytics Applications;2022 30th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP);2022-03

5. An FPGA Based Accelerator for Clustering Algorithms With Custom Instructions;IEEE Transactions on Computers;2021-05-01

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