Locally adaptive neighborhood selection for collaborative filtering recommendations

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

Volumn 5149 LNCS, Issue , 2008, Pages 22-31

  Baltrunas, Linas ^a   Ricci, Francesco ^a  

^a FREE UNIVERSITY OF BOZEN BOLZANO   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

CORRELATION METHODS; INFORMATION RETRIEVAL SYSTEMS; SIGNAL FILTERING AND PREDICTION; STANDARDS; TARGETS; WORLD WIDE WEB;

ADAPTIVE HYPERMEDIA; ADAPTIVE NEIGHBORHOOD SELECTION; ADAPTIVE WEB; COLLABORATIVE FILTERING; ERROR MEASURES; FUNDAMENTAL COMPONENTS; INTERNATIONAL CONFERENCES; ITEM SELECTION; RECOMMENDER SYSTEMS;

ADAPTIVE SYSTEMS;

EID: 52949119596     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-70987-9_5     Document Type: Conference Paper

References (16)

1. Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: The Adaptive Web, pp. 291-324. Springer, Heidelberg (2007)
2. Wettschereck, D., Aha, D.W., Mohri, T.: A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artif. Intell. Rev. 11(1-5), 273-314 (1997)
3. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2), 273-324 (1997)
4. Radlinski, F., Joachims, T.: Query chains: learning to rank from implicit feedback. In: KDD 2005: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pp. 239-248. ACM Press, New York (2005)
5. Geng, X., Liu, T.-Y., Qin, T., Li, H.: Feature selection for ranking. In: SIGIR, 2007: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 407-414. ACM, New York (2007)
6. Atkeson, C., Andrew Moore, S.S.: Locally weighted learning. AI Review 11, 11-73 (1997)
7. Puuronen, S., Tsymbal, A.: Local feature selection with dynamic integration of classifiers (2001)
8. Yu, K., Xu, X., Ester, M., Kriegel, H.-P.: Feature weighting and instance selection for collaborative filtering: An information-theoretic approach*. Knowl. Inf. Syst. 5(2), 201-224 (2003)
9. Jin, R., Chai, J.Y., Si, L.: An automatic weighting scheme for collaborative filtering. In: SIGIR, 2004: Proceedings of the 27th annual international ACM SIGIR, conference on Research and development in information retrieval, pp. 337-344. ACM Press, New York (2004)
10. Breese, J.S., Heckerman, D., Kadie, C.M.: Empirical analysis of predictive algorithms for collaborative filtering. In: Cooper, G.F., Moral, S. (eds.) UAI, pp. 43-52. Morgan Kaufmann, San Francisco (1998)
11. Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR, pp. 230-237. ACM, New York (1999)
12. Berkovsky, S., Kuflik, T., Ricci, F.: Cross-domain mediation in collaborative filtering. In: Conati, C., McCoy, K.F., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 355-359. Springer, Heidelberg (2007)
13. MovieLens: Movielens dataset, http://www.grouplens.org/
14. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5-53 (2004)
15. Yahoo!: Webscope movie data set (Version 1.0), http://research.yahoo.com/
16. Robnik-Šikonja, M., Kononenko, I.: Theoretical and empirical analysis of relieff and rrelieff. Mach. Learn. 53(1-2), 23-69 (2003)