Enhanced user modeling based on link attributes for recommendation system-Reference-Cited by-同舟云学术

Enhanced user modeling based on link attributes for recommendation system

Published:2016 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 18th Annual International Conference on Electronic Commerce e-Commerce in Smart connected World - ICEC '16
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Author:

Ha Inay¹,Oh Kyeong-Jin¹,Hong Myung-Duk¹,Jo Geun-Sik¹

Affiliation:

1. Inha University, Incheon, South Korea

Publisher

ACM Press

Reference19 articles.

1. Al-taie, M., and Kadry, S. 2012. Applying Social Network Analysis to Analyze a Web-based Community. Advanced Computer Science and Applications 3, 2 (Feb. 2012), 29--41.

2. Bellogin, A., Cantador, I., Diez, F., Castells, P., and Chavarriaga, E. 2013. An Empirical Comparison of Social, Collaborative Filtering, and Hybrid Recommenders. ACM Transactions on Intelligent Systems and Technology 4, 1 (Jan. 2013), 1--29.

3. Bourke, S., McCarthy, K., and Smyth, B. 2011. Power To The People: Exploring Neighbourhood Formations In Social Recommender Systems. In Proceedings of the 15th ACM Conference on Recommender Systems (Chicago, IL, USA, October 23-27, 2011).

4. Candillier, L., Meyer, F., and Fessant, F. 2008. Designing Specific Weighted Similarity Measures to Improve Collaborative Filtering Systems. In Proceedings of the 8th Industrial Conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects (Leipzig, Germany, July 16-18, 2008).

5. DuBois, T., Golbeck, J., Kleint, J., and Srinivasan, A. 2009. Improving Recommendation Accuracy by Clustering Social Networks with Trust. In Proceedings of the 3rd ACM Conference on Recommender Systems Workshop: Recommender Systems and the Social Web (Shanghai, China, November 3-7, 2014).