A hotel recommendation system based on collaborative filtering and Rankboost algorithm

2010 International Conference on MultiMedia and Information Technology, MMIT 2010

Volumn 1, Issue , 2010, Pages 317-320

  Gao, Huming ^a   Li, Weili ^a  

^a TIANJIN UNIVERSITY OF FINANCE AND ECONOMICS   (China)

Author keywords

Collaborative filtering; Rankboost algorithm; Recommendation system

Indexed keywords

AS CONTENT; COLD-START; COLLABORATIVE FILTERING; COLLABORATIVE FILTERING METHODS; E-COMMERCE; HYBRID RECOMMENDATION; MARKETING CAPABILITY; NETWORK TECHNOLOGIES; PERSONALIZATIONS; PERSONALIZED RECOMMENDATION SYSTEMS; POTENTIAL USERS; RANKBOOST; RANKBOOST ALGORITHM; RAPID DEVELOPMENT; RECOMMENDATION SYSTEMS; SCALABILITY PROBLEMS; TECHNOLOGY-BASED;

ASSOCIATIVE PROCESSING; ELECTRONIC COMMERCE; HOTELS; INFORMATION TECHNOLOGY; KNOWLEDGE BASED SYSTEMS; SIGNAL FILTERING AND PREDICTION;

CLUSTERING ALGORITHMS;

EID: 77954257906     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MMIT.2010.14     Document Type: Conference Paper

References (10)

1. Deng Xiaohui, Qi Qiang. Analysis of e-commerce recommendation system[J]. Enterprise economy, 2007, (8):116-117.
2. Zen Chun, Xing Chunxiao, Zhou Lizhu. Personalization services technology [J]. Journal of Software, 2002, 13 (10):1953-1955.
3. Liu Pingfeng, Nie Guihua, Chen Donglin. E-commerce recommendation system survey [J]. Intelligence magazine, 2007, (9):46-47.
4. QUANT, FUYUKII, SHINICHIH. Improving accuracy of recommender system by clustering items based on stability of user similarity [C]//Proceedings of International Conference on IAWTIC. Washington, DC: IEEE Computer Society, 2006: 61.
5. Zha Wenqin, Liang Changyong, Cao Lei. Collaborative filtering Recommendation based on user clustering [J]. Computer technology and development, 2009, 19 (6):70-72.
6. Wang Hongchao, Chen Weiru, LIU Jun. Of goods based on customer clustering recommended method of study [J]. Computer technology and development, 2008, 18 (7):212-214.
7. Wang Hui, Gao Jun, Wang Zhongting. Personalization services of user-based clustering collaborative filtering recommendation [J]. Computer Applications, 2007, 27 (5):1225-1227.
8. Freund Y, Iyer R, Schapire RE, et al. An efficient boosting algorithm for combining preferences [J]. Journal of Machine L earning Research, 2003 (4):933 - 969.
9. Li Tao, Wang Dong, Ye leap, et al. A collaborative filtering recommendation algorithm based on user clustering [J]. Systems Engineering and Electronics, 2007, 29 (7):1178-1182.
10. Sun Shouyi, Wang Wei. A kind of clustering collaborative filtering based on user-personalized book recommendation system [J]. Modern Information, 2007, (11):140-141.