Top-k retrieval using facility location analysis

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

Volumn 7224 LNCS, Issue , 2012, Pages 305-316

  Zuccon, Guido ^a,d   Azzopardi, Leif ^a   Zhang, Dell ^b   Wang, Jun ^c  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

^b UNIVERSITY OF LONDON   (United Kingdom)

^c UNIVERSITY COLLEGE LONDON   (United Kingdom)

^d CSIRO   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS TECHNIQUES; FACILITY LOCATIONS; OBNOXIOUS FACILITIES; OPTIMAL SETS; PORTFOLIO THEORIES; RELEVANT DOCUMENTS; RETRIEVAL MODELS; SEARCH RESULTS;

LOCATION;

INFORMATION RETRIEVAL;

EID: 84860145463     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-28997-2_26     Document Type: Conference Paper

References (18)

1. Carbonell, J.G., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st ACM SIGIR, Melb., Australia, pp. 335-336 (1998)
2. Carterette, B.: An analysis of np-completeness in novelty and diversity ranking. Information Retrieval 14, 89-106 (2011)
3. Chandar, P., Carterette, B.: Diversification of search results using webgraphs. In: Proceeding of the 33rd International ACM SIGIR, pp. 869-870 (2010)
4. Chapelle, O., Metlzer, D., Zhang, Y., Grinspan, P.: Expected reciprocal rank for graded relevance. In: Proceeding of the 18th ACM CIKM, pp. 621-630 (2009)
5. Chen, H., Karger, D.R.: Less is more: Probabilistic models for retrieving fewer relevant documents. In: Proceedings of the 29th ACM SIGIR, Seattle, WA, USA, pp. 429-436 (2006)
6. Clarke, C.L.A., Kolla, M., Cormack, G.V., Vechtomova, O., Ashkan, A., Buttcher, S., MacKinnon, I.: Novelty and diversity in information retrieval evaluation. In: Proceedings of the 31st ACM SIGIR, Singapore, pp. 659-666 (2008)
7. Gollapudi, S., Sharma, A.: An axiomatic approach for result diversification. In: Proceedings of the 18th WWW, Madrid, Spain, pp. 381-390 (2009)
8. Gonzalez, T.F. (ed.): Handbook of Approximation Algorithms and Metaheuristics (2007)
9. Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann (2005)
10. J̈arvelin, K., Kek̈al̈ainen, J.: Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS) 20(4), 422-446 (2002) (Pubitemid 44642296)
11. Kurland, O., Lee, L.: PageRank without hyperlinks: Structural re-ranking using links induced by language models. In: Proceedings of the 28th ACM SIGIR, Salvador, Brazil, pp. 306-313 (2005)
12. Manning, C.D., Raghavan, P., Scḧutze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)
13. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall (2009)
14. Santos, R.L.T., Macdonald, C., Ounis, I.: Intent-aware search result diversification. In: Proceedings of the 34th ACM SIGIR, pp. 595-604 (2011)
15. Wang, J., Zhu, J.: Portfolio theory of information retrieval. In: Proceedings of the 32nd ACM SIGIR, Boston, MA, USA, pp. 115-122 (2009)
16. Zhai, C.: Statistical Language Models for Information Retrieval. Morgan and Claypool (2008)
17. Zhai, C., Cohen, W.W., Lafferty, J.D.: Beyond independent relevance: Methods and evaluation metrics for subtopic retrieval. In: Proceedings of the 26th ACM SIGIR, TO, Canada, pp. 10-17 (2003)
18. Zuccon, G., Azzopardi, L.: Using the Quantum Probability Ranking Principle to Rank Interdependent Documents. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., R̈uger, S., van Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 357-369. Springer, Heidelberg (2010)