Kyung Soo Kim
ABSTRACT
In this paper, we propose content-metadata based combined approach to effective collaborative recommendation. Our approach combines user-item rating scores and/or trust network information with content-metadata compensatively for boosting collaborative recommendation. In experiment, we identified that our approach could considerably improve recommendation performance when compared to existing collaborative recommendation methods.
- A. Kumar and A. Sharma. "Alleviating Sparsity and Scalability Issues in Collaborative Filtering Based Recommender Systems," In Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA), 2013.Google Scholar
- Suyun, W., Y. Ning, et al. "Collaborative Filtering Recommendation Algorithm Based on Item Clustering and Global Similarity," Business Intelligence and Financial Engineering (BIFE), 2012 Fifth International Conference on, 2012. Google ScholarDigital Library
- N Pang and ZD Lai. "Signature Word Extracting Research Based On Web Metadata," Instrumentation & Measurement, Sensor Network and Automation (IMSNA), 2012 International Symposium on, 2012.Google Scholar
- Prem Melville, Raymod J. Mooney and Ramadass Nagarajan, "Content-boosted collaborative filtering for improved recommendations," Preceeding of Eighteenth national conference on Artificial intelligence, Edmonton, Canada, pp. 187~192, July, 2002. Google ScholarDigital Library
- P. Massa and P. Avesani. "Trust-aware recommender systems," In Proceeding of the ACM Recommender Systems Conference (RecSys), 2007. Google ScholarDigital Library
- M. Jamali and M. Ester. "TrustWalker: a random walk model for combining trust-based and item-based recommendation," In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009. Google ScholarDigital Library
- Mukund Deshpande and George Karypis. 2004. Item-based top-N recommendation algorithms. ACM Trans. Inf. Syst. 22, 1 (January 2004), 143--177. DOI=10.1145/963770.963776 Google ScholarDigital Library
- San-Yih Hwang and Lung-Shian Chen. 2009. Using trust for collaborative filtering in eCommerce. In Proceedings of the 11th International Conference on Electronic Commerce (ICEC '09). ACM, New York, NY, USA, 240--248. DOI=10.1145/1593254.1593291 Google ScholarDigital Library
- Michael J. Pazzani and Daniel Billsus. 2007. Content-based recommendation systems. In The adaptive web, Peter Brusilovsky, Alfred Kobsa, and Wolfgang Nejdl (Eds.). Lecture Notes In Computer Science, Vol. 4321. Springer-Verlag, Berlin, Heidelberg 325--341. Google ScholarDigital Library
Index Terms
- Metadata based combined approach for effective collaborative recommendation
Recommendations
Collaborative recommendation: A robustness analysis
Collaborative recommendation has emerged as an effective technique for personalized information access. However, there has been relatively little theoretical analysis of the conditions under which the technique is effective. To explore this issue, we ...
An improved collaborative recommendation algorithm based on optimized user similarity
There are lots of issues existing in traditional collaborative filtering recommendation, such as data scarcities, cold start, recommendation accuracy and timeliness. And how to improve the efficiency and quality of recommendation is a key problem in ...
Recommendation algorithm combining the user-based classified regression and the item-based filtering
ICEC '06: Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internetWith the expansion of the Internet services, providing personalized product recommendations has become one of the most important ways to attract customers. Especially, collaborative recommender systems have achieved widespread success on the web. ...
Comments