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Improving Result Diversity Using Query Term Proximity in Exploratory Search

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Big Data Analytics (BDA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11932))

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Abstract

In the information retrieval system, relevance manifestation is pivotal and regularly based on document-term statistics, i.e. term frequency (tf), inverse document frequency (idf), etc. Query term proximity within matched documents is mostly under-explored. In this paper, a novel information retrieval framework is proposed, to promote the documents among all relevant retrieved ones. The relevance estimation is a weighted combination of document statistics and query term statistics, and term-term proximity is a simply aggregates of diverse user preferences aspects in query formation, thus adapted into the framework with conventional relevance measures. Intuitively, QTP is exploited to promote the documents for balanced exploitation-exploration, and eventually navigate a search towards goals. The evaluation asserts the usability of QTP measures to balance several seeking tradeoffs, e.g. relevance, novelty, result diversity (Coverage and Topicality), and overall retrieval. The assessment of user search trails indicates significant growth in a learning outcome.

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References

  1. White, R.W., Roth, R.A.: Exploratory search: beyond the query-response paradigm. Synthesis Lect. Inform. Concepts Retrieval Serv. 1(1), 1–98 (2009)

    Article  Google Scholar 

  2. Idreos, S., Papaemmanouil, O., Chaudhuri, S.: Overview of data exploration techniques. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 277–281. ACM, May 2015

    Google Scholar 

  3. Marchionini, G.: Exploratory search: from finding to understanding. Commun. ACM 49(4), 41–46 (2006)

    Article  Google Scholar 

  4. Kersten, M.L., Idreos, S., Manegold, S., Liarou, E.: The researcher’s guide to the data deluge: querying a scientific database in just a few seconds. PVLDB Chall. Vis. 3(3) (2011)

    Google Scholar 

  5. Singh, V.: Predicting search intent based on in-search context for exploratory search. Int. J. Adv. Pervasive Ubiquit. Comput. (IJAPUC) 11(3), 53–75 (2019)

    Article  Google Scholar 

  6. Van Rijsbergen, C.J.: A theoretical basis for the use of co-occurrence data in information retrieval. J. Doc. 33(2), 106–119 (1977)

    Article  Google Scholar 

  7. Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24(5), 513–523 (1988)

    Article  Google Scholar 

  8. Cosijn, E., Ingwersen, P.: Dimensions of relevance. Inf. Process. Manage. 36(4), 533–550 (2000)

    Article  Google Scholar 

  9. Barry, C.L.: User-defined relevance criteria: an exploratory study. J. Am. Soc. Inform. Sci. 45(3), 149–159 (1994)

    Article  Google Scholar 

  10. Rasolofo, Y., Savoy, J.: Term proximity scoring for keyword-based retrieval systems. In: Sebastiani, F. (ed.) ECIR 2003. LNCS, vol. 2633, pp. 207–218. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36618-0_15

    Chapter  MATH  Google Scholar 

  11. Qiao, Y.N., Du, Q., Wan, D.F.: A study on query terms proximity embedding for information retrieval. Int. J. Distrib. Sens. Netw. 13(2), 1550147717694891 (2017)

    Article  Google Scholar 

  12. Keen, E.M.: Some aspects of proximity searching in text retrieval systems. J. Inform. Sci. 18(2), 89–98 (1992)

    Article  Google Scholar 

  13. Beigbeder, M., Mercier, A.: An information retrieval model using the fuzzy proximity degree of term occurrences. In: Proceedings of the 2005 ACM Symposium on Applied Computing, pp. 1018–1022. ACM, March 2005

    Google Scholar 

  14. Büttcher, S., Clarke, C.L., Lushman, B.: Term proximity scoring for ad-hoc retrieval on very large text collections. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 621–622. ACM, August 2006

    Google Scholar 

  15. Schenkel, R., Broschart, A., Hwang, S., Theobald, M., Weikum, G.: Efficient text proximity search. In: Ziviani, N., Baeza-Yates, R. (eds.) SPIRE 2007. LNCS, vol. 4726, pp. 287–299. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75530-2_26

    Chapter  Google Scholar 

  16. Svore, K.M., Kanani, P.H., Khan, N.:. How good is a span of terms?: exploiting proximity to improve web retrieval. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 154–161. ACM, July 2010

    Google Scholar 

  17. Zhao, J., Yun, Y.: A proximity language model for information retrieval. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 291–298. ACM, July 2009

    Google Scholar 

  18. He, B., Huang, J.X., Zhou, X.: Modeling term proximity for probabilistic information retrieval models. Inf. Sci. 181(14), 3017–3031 (2011)

    Article  MathSciNet  Google Scholar 

  19. Sadakane, K., Imai, H.: Text retrieval by using k-word proximity search. In: Proceedings of 1999 International Symposium on Database Applications in Non-Traditional Environments (DANTE 1999) (Cat. No. PR00496), pp. 183–188. IEEE (1999)

    Google Scholar 

  20. Borlund, P.: The IIR evaluation model: a framework for evaluation of interactive information retrieval systems. Inform. Res. Int. Electron. J. 8(3) (2003)

    Google Scholar 

  21. Song, R., Taylor, M.J., Wen, J.-R., Hon, H.-W., Yu, Y.: Viewing term proximity from a different perspective. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 346–357. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78646-7_32

    Chapter  Google Scholar 

  22. Miao, J., Huang, J.X., Ye, Z.: Proximity-based rocchio’s model for pseudo relevance. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 535–544. ACM, August 2012

    Google Scholar 

  23. Zhao, J., Huang, J.X., Ye, Z.: Modeling term associations for probabilistic information retrieval. ACM Trans. Inform. Syst. (TOIS) 32(2), 7 (2014)

    Google Scholar 

  24. Ye, Z., He, B., Wang, L., Luo, T.: Utilizing term proximity for blog post retrieval. J. Am. Soc. Inform. Sci. Technol. 64(11), 2278–2298 (2013)

    Article  Google Scholar 

  25. Saracevic, T.: The notion of relevance in information science: everybody knows what relevance is. But, what is it really? Synthesis Lect. Inform. Concepts Retrieval Serv. 8(3), i-109 (2016)

    Google Scholar 

  26. Borlund, P.: The concept of relevance in IR. J. Am. Soc. Inform. Sci. Technol. 54(10), 913–925 (2003)

    Article  Google Scholar 

  27. Drosou, M., Pitoura, E.: YmaLDB: exploring relational databases via result-driven recommendations. VLDB J.—Int. J. Very Large Data Bases 22(6), 849–874 (2013)

    Article  Google Scholar 

  28. Dimitriadou, K., Papaemmanouil, O., Diao, Y.: Explore-by-example: an automatic query steering framework for interactive data exploration. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 517–528. ACM, June 2014

    Google Scholar 

  29. Ruotsalo, T., et al.: IntentRadar: search user interface that anticipates user’s search intents. In: CHI 2014 Extended Abstracts on Human Factors in Computing Systems, pp. 455–458. ACM, April 2014

    Google Scholar 

  30. di Sciascio, C., Sabol, V., Veas, E.E.: Rank as you go: user-driven exploration of search results. In: Proceedings of the 21st International Conference on Intelligent User Interfaces, pp. 118–129. ACM, March 2016

    Google Scholar 

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Authors and Affiliations

  1. Computer Engineering Department, National Institute of Technology, Kurukshetra, Kurukshetra, 136119, Haryana, India

    Vikram Singh & Mayank Dave

Authors
  1. Vikram Singh
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  2. Mayank Dave
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Correspondence to Vikram Singh .

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Editors and Affiliations

  1. Missouri University of Science and Technology, Rolla, MO, USA

    Sanjay Madria

  2. Harbin Institute of Technology, Shenzhen, China

    Philippe Fournier-Viger

  3. Ahmedabad University, Ahmedabad, India

    Sanjay Chaudhary

  4. International Institute of Information Technology, Hyderabad, India

    P. Krishna Reddy

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Singh, V., Dave, M. (2019). Improving Result Diversity Using Query Term Proximity in Exploratory Search. In: Madria, S., Fournier-Viger, P., Chaudhary, S., Reddy, P. (eds) Big Data Analytics. BDA 2019. Lecture Notes in Computer Science(), vol 11932. Springer, Cham. https://doi.org/10.1007/978-3-030-37188-3_5

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  • DOI: https://doi.org/10.1007/978-3-030-37188-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37187-6

  • Online ISBN: 978-3-030-37188-3

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