Predicting the performance of recommender systems: An information theoretic approach

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

Volumn 6931 LNCS, Issue , 2011, Pages 27-39

  Bellogín, Alejandro ^a   Castells, Pablo ^a   Cantador, Iván ^a  

^a UNIVERSIDAD AUTÓNOMA DE MADRID   (Spain)

Author keywords

language models; performance prediction; recommender systems

Indexed keywords

INFORMATION-THEORETIC APPROACH; LANGUAGE MODEL; PERFORMANCE PREDICTION; PERFORMANCE PREDICTOR; RECOMMENDATION METHODS;

COMPUTATIONAL LINGUISTICS; FORECASTING; INFORMATION THEORY; RECOMMENDER SYSTEMS;

SEARCH ENGINES;

EID: 80052972168     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23318-0_5     Document Type: Conference Paper

References (20)

1. Adomavicius, G., Tuzhilin, T.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowl. and Data Eng. 17(6), 734-749 (2005)
2. Amati, G., Carpineto, C., Romano, G.: Query difficulty, robustness, and selective application of query expansion. In: McDonald, S., Tait, J.I. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 127-137. Springer, Heidelberg (2004)
3. Bellogín, A., Castells, P.: A performance prediction approach to enhance collaborative filtering performance. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., van Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 382-393. Springer, Heidelberg (2010)
4. Bellogín, A., Wang, J., Castells, P.: Text retrieval methods for item ranking in collaborative filtering. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 301-306. Springer, Heidelberg (2011)
5. Buckley, C.: Topic prediction based on comparative retrieval rankings. In: 27th ACM Conference on Research and Development in Information Retrieval, pp. 506-507. ACM Press, New York (2004)
6. Cronen-Townsend, S., Zhou, Y., Croft, W.B.: Predicting query performance. In: 25th ACM Conference on Research and Development in Information Retrieval, pp. 299-306. ACM Press, New York (2002)
7. Cronen-Townsend, S., Zhou, Y., Croft, W.B.: A framework for selective query expansion. In: 13th ACM Conference on Information and Knowledge Management, pp. 236-237. ACM Press, New York (2004)
8. Dang, V., Bendersky, M., Croft, W.B.: Learning to rank query reformulations. In: 33rd ACM Conference on Research and Development in Information Retrieval, pp. 807-808. ACM Press, New York (2010)
9. Hauff, C.: Predicting the Effectiveness of Queries and Retrieval Systems. PhD thesis, University of Twente, Enschede (2010)
10. Hauff, C., Hiemstra, D., De Jong, F.: A Survey of Pre-Retrieval Query Performance Predictors. In: 17th ACM Conference on Information and Knowledge Management, pp. 439-448. ACM Press, New York (2008)
11. He, B., Ounis, I.: Inferring query performance using pre-retrieval predictors. In: Apostolico, A., Melucci, M. (eds.) SPIRE 2004. LNCS, vol. 3246, pp. 43-54. Springer, Heidelberg (2004)
12. Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: 22nd ACM Conference on Research and Development in Information Retrieval, pp. 230-237. ACM Press, New York (1999)
13. Hofmann, T.: Latent semantic models for Collaborative filtering. ACM Trans. Inf. Syst. 22(1), 89-115 (2004)
14. Mothe, J., Tanguy, L.: Linguistic features to predict query difficulty. In: ACM SIGIR Workshop on Predicting Query Difficulty - Methods and Applications (2005)
15. Pérez-Iglesias, J., Araujo, L.: Ranking List Dispersion as a Query Performance Predictor. In: Azzopardi, L., Kazai, G., Robertson, S., Rüger, S., Shokouhi, M., Song, D., Yilmaz, E. (eds.) ICTIR 2009. LNCS, vol. 5766, pp. 371-374. Springer, Heidelberg (2009)
16. Plachouras, V., He, B., Ounis, I.: University of Glasgow at TREC2004: Experiments in Web, Robust and Terabyte tracks with Terrier. In: 13th Text Retrieval Conference, Gaithesburg, Maryland (2004)
17. Shtok, A., Kurland, O., Carmel, D.: Predicting Query Performance by Query-Drift Estimation. In: Azzopardi, L., Kazai, G., Robertson, S., Rüger, S., Shokouhi, M., Song, D., Yilmaz, E. (eds.) ICTIR 2009. LNCS, vol. 5766, pp. 305-312. Springer, Heidelberg (2009)
18. Wang, J., De Vries, A.P., Reinders, M.J.T.: Unified relevance models for rating prediction in collaborative filtering. ACM Trans. Inf. Syst. 26(3), 1-42 (2008)
19. Yom-Tov, E., Fine, S., Carmel, D., Darlow, A.: Learning to estimate query difficulty: including applications to missing content detection and distributed information retrieval. In: 28th ACM Conference on Research and Development in Information Retrieval, pp. 512-519. ACM Press, New York (2005)
20. Zhou, Y., Croft, B.W.: Query performance prediction in web search environments. In: 30th ACM Conference on Research and Development in Information Retrieval, pp. 543-550. ACM Press, New York (2007)