A collaborative filtering recommendation algorithm based on information theory and bi-clustering-Reference-Cited by-同舟云学术

A collaborative filtering recommendation algorithm based on information theory and bi-clustering

Published:2019-02-04 Issue:12 Volume:31 Page:8279-8287
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Jiang Mingyang,Zhang Zhifeng,Jiang Jingqing,Wang Qinghu,Pei Zhili

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-018-3959-2.pdf

Reference30 articles.

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2. Ekstrand MD, Riedl JT, Konstan JA (2011) Collaborative filtering recommender systems. Found Trends Hum-Comput Interact 4(2):81–173. https://doi.org/10.1561/1100000009

3. Barragáns-Martínez AB, Costa-Montenegro E, Burguillo JC, Rey-López M, Mikic-Fonte FA, Peleteiro A (2010) A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Inf Sci 180(22):4290–4311. https://doi.org/10.1016/j.ins.2010.07.024

4. Breese JS, Heckerman D, Kadie C (2013) Empirical analysis of predictive algorithms for collaborative filtering. CoRR abs/1301.7363. arXiv: 1301.7363

5. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp 285–295. https://doi.org/10.1145/371920.372071

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