Local Item-Item Models For Top-N Recommendation-Reference-Cited by-同舟云学术

Local Item-Item Models For Top-N Recommendation

Published:2016-09-07 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 10th ACM Conference on Recommender Systems
language:
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Author:

Christakopoulou Evangelia¹,Karypis George¹

Affiliation:

1. University of Minnesota, Twin Cities, Minneapolis, MN, USA

Funder

National Science Foundation

Intel Software and Services Group

Army Research Office

Digital Technology Center at the University of Minnesota

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/2959100.2959185

Reference19 articles.

1. Cluto clustering toolkit. http://glaros.dtc.umn.edu/gkhome/cluto/cluto/overview. Cluto clustering toolkit. http://glaros.dtc.umn.edu/gkhome/cluto/cluto/overview.

2. Flixster dataset. http://http://www.cs.sfu.ca/~sja25/personal/datasets/. Flixster dataset. http://http://www.cs.sfu.ca/~sja25/personal/datasets/.

3. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

4. F. Aiolli. A preliminary study on a recommender system for the million songs dataset challenge. PREFERENCE LEARNING: PROBLEMS AND APPLICATIONS IN AI page~1 2012. F. Aiolli. A preliminary study on a recommender system for the million songs dataset challenge. PREFERENCE LEARNING: PROBLEMS AND APPLICATIONS IN AI page~1 2012.

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