A coupled clustering approach for items recommendation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7819 LNAI, Issue PART 2, 2013, Pages 365-376

  Yu, Yonghong ^a   Wang, Can ^b   Gao, Yang ^a   Cao, Longbing ^b   Chen, Xixi ^c  

^a NANJING UNIVERSITY   (China)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^c Shandong Branch   (China)

Author keywords

Clustering algorithm; Collaborative filtering; Coupled object similarity; Recommender systems

Indexed keywords

CLUSTERING APPROACH; COLLABORATIVE FILTERING TECHNIQUES; CONTENT-BASED RECOMMENDATION; COUPLED OBJECTS; HYBRID RECOMMENDATION; INFORMATION OVERLOADS; PERSONALIZED INFORMATION; SCALABILITY PROBLEMS;

COLLABORATIVE FILTERING; DATA MINING; ELECTRONIC COMMERCE; RECOMMENDER SYSTEMS; SCALABILITY;

CLUSTERING ALGORITHMS;

EID: 84893531819     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-37456-2_31     Document Type: Conference Paper

References (13)

1. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734-749 (2005)
2. Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Communications of the ACM 40(3), 66-72 (1997)
3. Melville, P., Mooney, R., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Proceedings of the National Conference on Artificial Intelligence, pp. 187-192. AAAI Press, MIT Press (1999, 2002)
4. Wang, C., Cao, L., Wang, M., Li, J., Wei, W., Ou, Y.: Coupled nominal similarity in unsupervised learning. In: CIKM, pp. 973-978. ACM (2011)
5. Cao, L., Ou, Y., Yu, P.: Coupled behavior analysis with applications. IEEE Transactions on Knowledge and Data Engineering 24(8), 1378-1392 (2012)
6. Gan, G., Ma, C., Wu, J.: Data clustering: theory, algorithms & applications (asasiam series on statistics & applied probability, n 20). Recherche 67, 02 (2007)
7. Ahmad, A., Dey, L.: A k-mean clustering algorithm for mixed numeric and categorical data. Data & Knowledge Engineering 63(2), 503-527 (2007)
8. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: CSCW, pp. 175-186. ACM (1994)
9. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285-295. ACM (2001)
10. Krulwich, B., Burkey, C.: Learning user information interests through extraction of semantically significant phrases. In: Proceedings of the AAAI Spring Symposium on Machine Learning in Information Access, pp. 100-112 (1996)
11. Al Mamunur Rashid, S., Karypis, G., Riedl, J.: Clustknn: a highly scalable hybrid model-& memory-based cf algorithm. In: Proc. of WebKDD 2006, Citeseer (2006)
12. Xue, G., Lin, C., Yang, Q., Xi, W., Zeng, H., Yu, Y., Chen, Z.: Scalable collaborative filtering using cluster-based smoothing. In: SIGIR, pp. 114-121. ACM (2005)
13. Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery 2(3), 283-304 (1998)