Predicting User Preference Based on Matrix Factorization by Exploiting Music Attributes-Reference-Cited by-同舟云学术

Predicting User Preference Based on Matrix Factorization by Exploiting Music Attributes

Published:2016 Issue: Volume: Page:
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Container-title:Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering - C3S2E '16
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Author:

Nabizadeh Amir Hossein¹,Jorge Alípio Mário¹,Tang Suhua²,Yu Yi³

Affiliation:

1. LIAAD / INESC TEC, Porto, Portugal, DCC-FCUP, Universidade do Porto, Portugal

2. Graduate School of Informatics and Engineering, University of Electro-Communications

3. Digital Content and Media, Sciences Research Division, National Institute of Informatics

Publisher

ACM Press

Reference19 articles.

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3. Z. Gantner, L. Drumond, C. Freudenthaler, S. Rendle, and L. Schmidt-Thieme. Learning attribute-to-feature mappings for cold-start recommendations. In Data Mining (ICDM), 2010 IEEE 10th International Conference on, pages 176--185. IEEE, 2010.

4. Z. Gantner, S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Mymedialite: A free recommender system library. In Proceedings of the fifth ACM conference on Recommender systems, pages 305--308. ACM, 2011.

5. Y. Hang, Y. Hang, and W. Xingwei. A cold-start recommendation algorithm based on new user's implicit information and multi-attribute rating matrix. In Hybrid Intelligent Systems, 2009. HIS'09. Ninth International Conference on, volume 2, pages 353--358. IEEE, 2009.

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