skip to main content
10.1145/3278681.3278710acmotherconferencesArticle/Chapter ViewAbstractPublication PageshtConference Proceedingsconference-collections
research-article

Re-ranking the search results for users with time-periodic intents

Published:26 September 2018Publication History

ABSTRACT

This paper investigates the time of search as a feature to improve the personalization of information retrieval systems. In general, users issue small and ambiguous queries, which can refer to different topics of interest. Although personalized information retrieval systems take care of user's topics of interest, but they do not consider if the topics are time periodic. The same ranked list cannot satisfy user search intents every time. This paper proposes a solution to rerank the search results for time sensitive ambiguous queries. An algorithm "HighTime" is presented here to disambiguate the time sensitive ambiguous queries and re-rank the default Google results by using a time sensitive user profile. The algorithm is evaluated by using two comparative measures, MAP and NDCG.

Results from user experiments showed that re-ranking of search results based on HighTime is effective in presenting relevant results to the users.

References

  1. Eugene Agichtein, Eric Brill, and Susan Dumais. 2006. Improving Web Search Ranking by Incorporating User Behavior Information. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '06). ACM, New York, NY, USA, 19--26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Yossi Azar, Iftah Gamzu, and Xiaoxin Yin. 2009. Multiple intents re-ranking. In Proceedings of the 41st Annual ACM Symposium on Theory of Computing, STOC 2009. ACM, Bethesda, MD, USA, 669--678. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Steve Fox, Kuldeep Karnawat, Mark Mydland, Susan Dumais, and Thomas White. 2005. Evaluating Implicit Measures to Improve Web Search. ACM Trans. Inf. Syst. 23, 2 (April 2005), 147--168. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Carol Jean Godby. 1999. Wordnet: An Electronic Lexical Database. Christiane Fellbaum. The Library Quarterly 69, 3 (1999), 406--408.Google ScholarGoogle ScholarCross RefCross Ref
  5. Gregory Grefenstette. 1997. Short query linguistic expansion techniques: Palliating one-word queries by providing intermediate structure to text. In Information Extraction A Multidisciplinary Approach to an Emerging Information Technology. Springer, Frascati, Italy, 97--114. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Taher H Haveliwala. 2003. Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search. IEEE transactions on knowledge and data engineering 15, 4 (2003), 784--796. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Hua Jiang, Yong-Xing Ge, Dan Zuo, and Bing Han. 2008. TIMERANK: A method of improving ranking scores by visited time. In Machine Learning and Cybernetics, 2008 International Conference on, Vol. 3. IEEE, Kunming, China, 1654--1657.Google ScholarGoogle ScholarCross RefCross Ref
  8. Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, and Geri Gay. 2017. Accurately Interpreting Clickthrough Data As Implicit Feedback. SIGIR Forum 51, 1 (Aug. 2017), 4--11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Shahid Kamal, Roliana Ibrahim, and Imran Ghani. 2016. Post-search Ambiguous Query Classification Method Based on Contextual and Temporal Information. In Asian Conference on Intelligent Information and Database Systems. Springer, Berlin, Heidelberg, 575--583.Google ScholarGoogle Scholar
  10. Nattiya Kanhabua and Kjetil Nørvåg. 2010. Determining time of queries for re-ranking search results. In International Conference on Theory and Practice of Digital Libraries. Springer, Berlin, Heidelberg, 261--272. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Robert Krovetz and W Bruce Croft. 1992. Lexical ambiguity and information retrieval. ACM Transactions on Information Systems (TOIS) 10, 2 (1992), 115--141. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Sarvesh Kumar, SK Jain, and RM Sharma. 2014. Diversification of web search results using post-retrieval clustering. In Computer and Communication Technology (ICCCT), 2014 International Conference on. IEEE, 1--6.Google ScholarGoogle ScholarCross RefCross Ref
  13. Seung Eun Lee and Dongug Kim. 2013. A Click Model for Time-sensitive Queries. In Proceedings of the 22Nd International Conference on World Wide Web (WWW '13 Companion). ACM, New York, NY, USA, Article 2487859, 2 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Michael Lesk. 1986. Automatic Sense Disambiguation Using Machine Readable Dictionaries: How to Tell a Pine Cone from an Ice Cream Cone. In Proceedings of the 5th Annual International Conference on Systems Documentation (SIGDOC '86). ACM, New York, NY, USA, Article 318728, 3 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Yizhou Lu, Benyu Zhang, Wensi Xi, Zheng Chen, Yi Liu, Michael R. Lyu, and Wei-ying Ma. 2004. The Powerrank Web Link Analysis Algorithm. In Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers & Amp; Posters (WWW Alt. '04). ACM, New York, NY, USA, Article 1013422, 2 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Thomas Mandl. 2009. Artificial intelligence for information retrieval. In Encyclopedia of Artificial Intelligence. IGI Global, 151--156.Google ScholarGoogle Scholar
  17. Christopher D Manning, Prabhakar Raghavan, and Hinrich Schutze. 2009. An information to information retrieval. (2009).Google ScholarGoogle Scholar
  18. Jivashi Nagar and Hussein Suleman. 2017. Investigating Per-user Time Sensitivity of Search Topics. In Joint Proceedings of the 1st Workshop on Temporal Dynamics in Digital Libraries (TDDL 2017), the (Meta)-Data Quality Workshop (MDQual 2017) and the Workshop on Modeling Societal Future (Futurity 2017) co-located with 21st International Conference on Theory and Practice of Digital Libraries (TPLD 2017), Vol. 2038. CEUR-WS.org, Thessaloniki, Greece. http://ceur-ws.org/Vol-2038/paper2.pdfGoogle ScholarGoogle Scholar
  19. Antti Oulasvirta, Janne P. Hukkinen, and Barry Schwartz. 2009. When More is Less: The Paradox of Choice in Search Engine Use. In Proceedings of the 32Nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '09). ACM, New York, NY, USA, Article 1572030, 8 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Soo Young Rieh. 2003. Investigating Web searching behavior in home environments. Proceedings of the American Society for Information Science and Technology 40, 1 (2003), 255--264.Google ScholarGoogle ScholarCross RefCross Ref
  21. Stuart Rose, Dave Engel, Nick Cramer, and Wendy Cowley. 2010. Automatic keyword extraction from individual documents. Text Mining: Applications and Theory (2010), 1--20.Google ScholarGoogle Scholar
  22. Mark Sanderson. 2008. Ambiguous Queries: Test Collections Need More Sense. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '08). ACM, New York, NY, USA, 499--506. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Rodrygo L.T Santos, Craig Macdonald, and Iadh Ounis. 2010. Exploiting Query Reformulations for Web Search Result Diversification. In Proceedings of the 19th International Conference on World Wide Web (WWW '10). ACM, New York, NY, USA, Article 1772780, 10 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Md Shajalal, Md Zia Ullah, Abu Nowshed Chy, and Masaki Aono. 2016. Query subtopic diversification based on cluster ranking and semantic features. In Advanced Informatics: Concepts, Theory And Application (ICAICTA), 2016 International Conference On. IEEE, George Town, Malaysia, 1--6.Google ScholarGoogle Scholar
  25. H Sheng, AS Goker, and Daqing He. 2001. Web user search pattern analysis for modelling query topic changes. Lecture Notes in Computer Science 2109 (2001).Google ScholarGoogle Scholar
  26. Ahu Sieg, Bamshad Mobasher, and Robin Burke. 2007. Web Search Personalization with Ontological User Profiles. In Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management (CIKM '07). ACM, New York, NY, USA, Article 1321515, 10 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Mark D Smucker, James Allan, and Ben Carterette. 2007. A comparison of statistical significance tests for information retrieval evaluation. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management. ACM, New York, NY, USA, 623--632. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Ruihua Song, Zhenxiao Luo, Jian-Yun Nie, Yong Yu, and Hsiao-Wuen Hon. 2009. Identification of ambiguous queries in web search. Information Processing & Management 45, 2 (2009), 216--229. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Wei Song, Ying Liu, Lizhen Liu, and Hanshi Wang. 2016. EXAMINING PERSONALIZATION HEURISTICS BY TOPICAL ANALYSIS OF QUERY LOG. INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING, INFORMATION AND CONTROL 12, 5 (2016), 1745--1760.Google ScholarGoogle Scholar
  30. Jaime Teevan, Susan T Dumais, and Eric Horvitz. 2005. Personalizing search via automated analysis of interests and activities. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, New York, NY, USA, 449--456. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Jaime Teevan, Susan T. Dumais, and Eric Horvitz. 2010. Potential for Personalization. ACM Trans. Comput.-Hum. Interact. 17, 1, Article 4 (April 2010), 31 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Md Zia Ullah, Md Shajalal, Abu Nowshed Chy, and Masaki Aono. 2016. Query Subtopic Mining Exploiting Word Embedding for Search Result Diversification. In Information Retrieval Technology. Springer, 308--314.Google ScholarGoogle Scholar
  33. Alexander JAM Van Deursen and Jan AGM Van Dijk. 2009. Using the Internet: Skill related problems in users' online behavior. Interacting with computers 21, 5-6 (2009), 393--402. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Maksims Volkovs. 2015. Context Models For Web Search Personalization. CoRR abs/1502.00527 (2015). arXiv:1502.00527 http://arxiv.org/abs/1502.00527Google ScholarGoogle Scholar
  35. Thanh Tien Vu, Alistair Willis, and Dawei Song. 2015. Modelling time-aware search tasks for search personalisation. In Proceedings of the 24th International Conference on World Wide Web. ACM, New York, NY, USA, 131--132. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Deng Yi, Yin Zhang, and Baogang Wei. 2016. Query Subtopic Mining via Subtractive Initialization of Non-negative Sparse Latent Semantic Analysis. J. Inf. Sci. Eng. 32, 5 (2016), 1161--1181.Google ScholarGoogle Scholar

Index Terms

  1. Re-ranking the search results for users with time-periodic intents

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        SAICSIT '18: Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists
        September 2018
        362 pages
        ISBN:9781450366472
        DOI:10.1145/3278681

        Copyright © 2018 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 26 September 2018

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate187of439submissions,43%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader