Useful acquiring ratings for collaborative filtering

Proceedings - 2009 IEEE Youth Conference on Information, Computing and Telecommunication, YC-ICT2009

Volumn , Issue , 2009, Pages 483-486

  Zeng, Wei ^a   Shang, Ming Sheng ^a   Qian, Tie Yun ^b  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b WUHAN UNIVERSITY   (China)

Author keywords

Active learning; Algorithms; Collaborative filtering; Useful sampling

Indexed keywords

ACTIVE LEARNING; ACTIVE-LEARNING ALGORITHM; COLLABORATIVE FILTERING; CUSTOMER PREFERENCES; DATA SETS; NEW PRODUCT; POTENTIAL CUSTOMERS; PRODUCT RECOMMENDATION SYSTEM; RANDOM SAMPLING METHOD; SAMPLING ALGORITHM; SAMPLING METHOD;

CUSTOMER SATISFACTION; MERGERS AND ACQUISITIONS; SALES; SIGNAL FILTERING AND PREDICTION;

LEARNING ALGORITHMS;

EID: 77950902490     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/YCICT.2009.5382452     Document Type: Conference Paper

References (5)

1. Zan Huang. Selectively Acquiring Ratings for Product Recommendation. International Conference for Electronic Commerce, Minneapolis, MN, August 19-22, 2007.
2. Gediminas Adomavicius, Alexander Tuzhilin. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transaction on Knowledge and Data Engineering, VOL.17, NO.6, JUNE 2005
3. Ming-Sheng Shang, Zi-Ke Zhang, Tao Zhou, Yi-Cheng Zhang: Collaborative filtering with diffusion-based similarity on tripartite graphs CoRR abs/0906.5017: (2009)
4. Mohsen Jamali, Martin Ester. TrustWalker: A Random Walk Model for Combining Trust-based and Item-based Recommendation. KDD'09, June 28-July 1, 2009, Paris, Franc.
5. www.netflixprize.com