Diversification for Multi-domain Result Sets

  • Alessandro Bozzon
  • Marco Brambilla
  • Piero Fraternali
  • Marco Tagliasacchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7387)

Abstract

Multi-domain search answers to queries spanning multiple entities, like “Find a hotel in Milan close to a concert venue, a museum and a good restaurant”, by producing ranked sets of entity combinations that maximize relevance, measured by a function expressing the user’s preferences. Due to the combinatorial nature of results, good entity instances (e.g., five stars hotels) tend to appear repeatedly in top-ranked combinations. To improve the quality of the result set, it is important to balance relevance with diversity, which promotes different, yet almost equally relevant, entities in the top-k combinations. This paper explores two different notions of diversity for multi-domain result sets, compares experimentally alternative algorithms for the trade-off between relevance and diversity, and performs a user study for evaluating the utility of diversification in multi-domain queries.

References

  1. 1.
    Carbonell, J., Goldstein, J.: The use of mmr, diversity-based reranking for reordering documents and producing summaries. In: SIGIR 1998: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335–336. ACM, New York (1998)CrossRefGoogle Scholar
  2. 2.
    Ceri, S., Brambilla, M.: Search Computing Systems. In: Pernici, B. (ed.) CAiSE 2010. LNCS, vol. 6051, pp. 1–6. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Clarke, C.L., Kolla, M., Cormack, G.V., Vechtomova, O., Ashkan, A., Büttcher, S., MacKinnon, I.: Novelty and diversity in information retrieval evaluation. In: SIGIR 2008: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 659–666. ACM, New York (2008)CrossRefGoogle Scholar
  4. 4.
    Demidova, E., Fankhauser, P., Zhou, X., Nejdl, W.: Divq: diversification for keyword search over structured databases. In: SIGIR 2010: Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 331–338. ACM, New York (2010)CrossRefGoogle Scholar
  5. 5.
    Dou, Z., Hu, S., Chen, K., Song, R., Wen, J.-R.: Multi-dimensional search result diversification. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, pp. 475–484. ACM, New York (2011)CrossRefGoogle Scholar
  6. 6.
    Drosou, M., Pitoura, E.: Search result diversification. SIGMOD Rec. 39(1), 41–47 (2010)CrossRefGoogle Scholar
  7. 7.
    Giunchiglia, F., Kharkevich, U., Zaihrayeu, I.: Concept search: Semantics enabled syntactic search. In: SemSearch, pp. 109–123 (2008)Google Scholar
  8. 8.
    Gollapudi, S., Sharma, A.: An axiomatic approach for result diversification. In: WWW 2009: Proceedings of the 18th International Conference on World Wide Web, pp. 381–390. ACM, New York (2009)CrossRefGoogle Scholar
  9. 9.
    Gonzalez, T.F.: Clustering to minimize the maximum intercluster distance. Theoretical Computer Science 38, 293–306 (1985)MathSciNetMATHCrossRefGoogle Scholar
  10. 10.
    Liu, Z., Sun, P., Chen, Y.: Structured search result differentiation. Proc. VLDB Endow. 2(1), 313–324 (2009)Google Scholar
  11. 11.
    Martinenghi, D., Tagliasacchi, M.: Proximity rank join. PVLDB 3(1), 352–363 (2010)Google Scholar
  12. 12.
    Rafiei, D., Bharat, K., Shukla, A.: Diversifying web search results. In: WWW 2010: Proceedings of the 19th International Conference on World Wide Web, pp. 781–790. ACM, New York (2010)CrossRefGoogle Scholar
  13. 13.
    Skoutas, D., Alrifai, M., Nejdl, W.: Re-ranking web service search results under diverse user preferences. In: PersDB 2010 (September 2010)Google Scholar
  14. 14.
    Soliman, M.A., Ilyas, I.F., Saleeb, M.: Building ranked mashups of unstructured sources with uncertain information. PVLDB 3(1), 826–837 (2010)Google Scholar
  15. 15.
    Vee, E., Srivastava, U., Shanmugasundaram, J., Bhat, P., Yahia, S.A.: Efficient computation of diverse query results. In: ICDE 2008: Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, pp. 228–236. IEEE Computer Society, Washington, DC (2008)CrossRefGoogle Scholar
  16. 16.
    Zhai, C.X., Cohen, W.W., Lafferty, J.: Beyond independent relevance: methods and evaluation metrics for subtopic retrieval. In: SIGIR 2003: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, pp. 10–17. ACM, New York (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alessandro Bozzon
    • 1
  • Marco Brambilla
    • 1
  • Piero Fraternali
    • 1
  • Marco Tagliasacchi
    • 1
  1. 1.Politecnico di MilanoMilanoItaly

Personalised recommendations