An exploration of improving prediction accuracy by constructing a multi-type clustering based recommendation framework-Reference-Cited by-同舟云学术

An exploration of improving prediction accuracy by constructing a multi-type clustering based recommendation framework

Published:2016-05 Issue: Volume:191 Page:388-397
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Ma Xiao,Lu Hongwei,Gan Zaobin,Zhao Qian

Funder

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference35 articles.

1. Toward the next generation of recommender systems;Adomavicius;IEEE Trans. Knowl. Data Eng.,2005

2. Matrix factorization techniques for recommender systems;Koren;Computer,2009

3. Latent semantic models for collaborative filtering;Hofmann;ACM Trans. Inf. Syst.,2004

4. Bayesian-inference-based recommendation in online social networks;Yang;IEEE Trans. Parallel Distrib. Syst.,2013

5. L.H. Ungar, D.P. Foster, Clustering methods for collaborative filtering, In: Proceedings of the AAAI Workshop on Recommendation Systems, vol. 1, 1998.

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