skip to main content
research-article

A Roadmap to User-Controllable Social Exploratory Search

Published:30 August 2019Publication History
Skip Abstract Section

Abstract

Information-seeking tasks with learning or investigative purposes are usually referred to as exploratory search. Exploratory search unfolds as a dynamic process where the user, amidst navigation, trial and error, and on-the-fly selections, gathers and organizes information (resources). A range of innovative interfaces with increased user control has been developed to support the exploratory search process. In this work, we present our attempt to increase the power of exploratory search interfaces by using ideas of social search—for instance, leveraging information left by past users of information systems. Social search technologies are highly popular today, especially for improving ranking. However, current approaches to social ranking do not allow users to decide to what extent social information should be taken into account for result ranking. This article presents an interface that integrates social search functionality into an exploratory search system in a user-controlled way that is consistent with the nature of exploratory search. The interface incorporates control features that allow the user to (i) express information needs by selecting keywords and (ii) to express preferences for incorporating social wisdom based on tag matching and user similarity. The interface promotes search transparency through color-coded stacked bars and rich tooltips. This work presents the full series of evaluations conducted to, first, assess the value of the social models in contexts independent to the user interface, in terms of objective and perceived accuracy. Then, in a study with the full-fledged system, we investigated system accuracy and subjective aspects with a structural model revealing that when users actively interacted with all of its control features, the hybrid system outperformed a baseline content-based–only tool and users were more satisfied.

References

  1. J.-W. Ahn and P. Brusilovsky. 2013. Adaptive visualization for exploratory information retrieval. Information Processing and Management 49, 5 (2013), 1139--1164. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Jae-Wook Ahn, Peter Brusilovsky, Jonathan Grady, Daqing He, and Radu Florian. 2010. Semantic annotation based exploratory search for information analysts. Information Processing and Management 46, 4 (2010), 383--402. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. J.-W. Ahn, P. Brusilovsky, D. He, J. Grady, and Q. Li. 2008. Personalized web exploration with task models. In Proceedings of the 17th International Conference on World Wide Web (WWW’08). 1--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. J.-W. Ahn, R. Farzan, and P. Brusilovsky. 2006. Social search in the context of social navigation. Journal of the Korean Society for Information Management 23, 2 (2006), 147--165.Google ScholarGoogle ScholarCross RefCross Ref
  5. J. C. Anderson and D. W. Gerbing. 1988. Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin 103, 3 (5 1988), 411--423.Google ScholarGoogle Scholar
  6. Sylwester Arabas, Michael R. Bareford, Lakshitha R. de Silva, Ian P. Gent, Benjamin M. Gorman, Masih Hajiarabderkani, Tristan Henderson, et al. 2014. Case studies and challenges in reproducibility in the computational sciences. arXiv:1408.2123.Google ScholarGoogle Scholar
  7. A. Aula. 2005. Studying User Strategies and Characteristics for Developing Web Search Interfaces. Ph.D. Dissertation. University of Tampere.Google ScholarGoogle Scholar
  8. Anne Aula and Klaus Nordhausen. 2006. Modeling successful performance in Web searching. Journal of the American Society for Information Science and Technology 57, 12 (2006), 1678--1693. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. R. Baeza-Yates, C. Hurtado, and M. Mendoza. 2004. Query recommendation using query logs in search engines. In Proceedings of the 2004 International Conference on Current Trends in Database Technology (EDBT’04). 588--596. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. S. Bao, G. Xue, X. Wu, Y. Yu, B. Fei, and Z. Su. 2007. Optimizing web search using social annotations. In Proceedings of the 16th International Conference on World Wide Web (WWW’07). ACM, New York, NY, 501--510. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. S. Bostandjiev, J. O’Donovan, and T. Höllerer. 2012. TasteWeights: A visual interactive hybrid recommender system. In Proceedings of the 6th ACM Conference on Recommender Systems (RecSys’12). ACM, New York, NY, 35--42. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. J. S. Breese, D. Heckerman, and C. Kadie. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI’98). 43--52. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. P. Brusilovsky, J. S. Oh, C. López, D. Parra, and W. Jeng. 2017. Linking information and people in a social system for academic conferences. New Review of Hypermedia and Multimedia 23, 2 (2017), 81--111. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Peter Brusilovsky, Barry Smyth, and Bracha Shapira. 2018. Social search. In Social Information Access. Springer International Publishing, 213--276.Google ScholarGoogle Scholar
  15. R. Burke. 2002. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12, 4 (2002), 331--370. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. D. N. Chin. 2001. Empirical evaluation of user models and user-adapted systems. User Modeling and User-Adapted Interaction 11, 1--2 (2001), 181--194. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. J. Chuang, C. D. Manning, and J. Heer. 2012. “Without the clutter of unimportant words”: Descriptive keyphrases for text visualization. ACM Transactions on Computer-Human Interaction 19, 3 (2012), Article 19, 29 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Ling Liu and M. Tamer Özsu (Eds.). 2009. Encyclopedia of database systems. MRR. Springer US, 1776. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. P. Cremonesi, Y. Koren, and R. Turrin. 2010. Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the 4th ACM Conference on Recommender Systems (RecSys’10). 39--46. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. C. di Sciascio, P. Brusilovsky, and E. Veas. 2018. A study on user-controllable social exploratory search. In Proceedings of the 23rd International Conference on Intelligent User Interfaces (IUI’18). ACM, New York, NY, 353--364. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. C. di Sciascio, V. Sabol, and E. Veas. 2016. Rank as you go: User-driven exploration of search results. In Proceedings of the 21st International Conference on Intelligent User Interfaces (IUI’16). ACM, New York, NY, 118--129. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. C. di Sciascio, V. Sabol, and E. Veas. 2017. Supporting exploratory search with a visual user-driven approach. ACM Transactions on Interactive Intelligent Systems 7, 4 (2017), 1--35. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Michael D. Ekstrand, Daniel Kluver, F. Maxwell Harper, and Joseph A. Konstan. 2015. Letting users choose recommender algorithms: An experimental study. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys’15). 11--18. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Brynn M. Evans and Ed H. Chi. 2010. An elaborated model of social search. Information Processing and Management 46, 6 (2010), 656--678. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. N. Ferro, N. Kando, N. Fuhr, M. Lippold, and J. Kalervo. 2016. Increasing reproducibility in IR: Findings from the Dagstuhl seminar on “Reproducibility of Data-Oriented Experiments in e-Science.” ACM SIGIR Forum 50, 1 (2016), 68--82. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Allen Foster and Nigel Ford. 2003. Serendipity and information seeking: An empirical study. Journal of Documentation 59, 3 (2003), 321--340.Google ScholarGoogle ScholarCross RefCross Ref
  27. J. Freyne and B. Smyth. 2004. An experiment in social search. In Adaptive Hypermedia and Adaptive Web-Based Systems. Springer, 95--103.Google ScholarGoogle Scholar
  28. Erick Gomez-Nieto, Frizzi San Roman, Paulo Pagliosa, Wallace Casaca, Elias S. Helou, Maria Cristina F. de Oliveira, and Luis Gustavo Nonato. 2014. Similarity preserving snippet-based visualization of web search results. IEEE Transactions on Visualization and Computer Graphics 20, 3 (2014), 457--470. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. C. He, D. Parra, and K. Verbert. 2016. Interactive recommender systems. Expert Systems with Applications 56, C (2016), 9--27. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. M. A. Hearst. 1995. TileBars: Visualization of term distribution information in full text information access. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’95). ACM, New York, NY, 59--66. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Marti A. Hearst. 2009. Search User Interfaces. Cambridge University Press, New York, NY. Google ScholarGoogle Scholar
  32. J. L. Herlocker, J. A. Konstan, and J. Riedl. 2000. Explaining collaborative filtering recommendations. In Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work (CSCW’00). 241--250. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. O. Hoeber and X. D. Yang. 2008. Evaluating WordBars in exploratory Web search scenarios. Information Processing and Management 44, 2 (2008), 485--510. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Kalervo Järvelin and Jaana Kekäläinen. 2002. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems 20, 4 (2002), 422--446. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Kalervo Järvelin, Susan L. Price, Lois M. L. Delcambre, and Marianne Lykke Nielsen. 2008. Discounted cumulated gain based evaluation of multiple-query IR sessions. In Proceedings of the European Conference on Information Retrieval. 4--15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Christine Jenkins, Cynthia L. Corritore, and Susan Wiedenbeck. 2003. Patterns of information seeking on the Web: A qualitative study of domain expertise and Web expertise. IT and Society 1, 3 (2003), 64--89.Google ScholarGoogle Scholar
  37. T. Joachims and F. Radlinski. 2007. Search engines that learn from implicit feedback. Computer 40, 8 (2007), 34--40. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Bart P. Knijnenburg, Svetlin Bostandjiev, John O’Donovan, and Alfred Kobsa. 2012. Inspectability and control in social recommenders. In Proceedings of the 6th ACM Conference on Recommender Systems (RecSys’12). 43--50. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. B. P. Knijnenburg, N. J. M. Reijmer, and M. C. Willemsen. 2011. Each to his own: How different users call for different interaction methods in recommender systems. In Proceedings of the 5th ACM Conference on Recommender Systems (RecSys’11). ACM, New York, NY, 141--148. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Bart P. Knijnenburg, Martijn C. Willemsen, Zeno Gantner, Hakan Soncu, and Chris Newell. 2012. Explaining the user experience of recommender systems. User Modelling and User-Adapted Interaction 22, 4--5 (2012), 441--504. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Beate Krause, Robert Jäschke, Andreas Hotho, and Gerd Stumme. 2008. Logsonomy—Social information retrieval with logdata. In Proceedings of the 19th ACM Conference on Hypertext and Hypermedia (HT’08). 157--166. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. J. R. Lewis and J. Sauro. 2009. The factor structure of the system usability scale. Lecture Notes in Computer Science, Vol. 5619. Springer, 940--103. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. G. Marchionini. 2006. Exploratory search: From finding to understanding. Communications of the ACM 49, 4 (2006), 41--46. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. G. Marchionini and B. Shneiderman. 1988. Finding facts vs. browsing knowledge in hypertext systems. Computer 21, 1 (1988), 70--80. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. A. Micarelli, F. Gasparetti, F. Sciarrone, and S. Gauch. 2007. Personalized search on the World Wide Web. In The Adaptive Web. Springer-Verlag, Berlin, Germany, 195--230. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Raquel Navarro-Prieto, Mike Scaife, and Yvonne Rogers. 1999. Cognitive strategies in web searching. In Proceedings of the 5th Conference on Human Factors and the Web. 43--56.Google ScholarGoogle Scholar
  47. Tien Nguyen and Jun Zhang. 2006. A novel visualization model for web search results. IEEE Transactions on Visualization and Computer Graphics 12, 5 (2006), 981--988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. K. A. Olsen, R. R. Korfhage, K. M. Sochats, M. B. Spring, and J. G. Williams. 1993. Visualization of a document collection: The vibe system. Information Processing and Management 29, 1 (1993), 69--81. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Ruth A. Palmquist and Kyung-Sun Kim. 2000. Cognitive style and on-line database search experience as predictors of Web search performance. Journal of the American Society for Information Science 51, 6 (2000), 558--566. Google ScholarGoogle ScholarCross RefCross Ref
  50. D. Parra, P. Brusilovsky, and C. Trattner. 2014. See what you want to see: Visual user-driven approach for hybrid recommendation. In Proceedings of the 19th International Conference on Intelligent User Interfaces (ACM IUI’14). ACM, New York, NY, 235--240. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. J. Pickens, G. Golovchinsky, C. Shah, P. Qvarfordt, and M. Back. 2008. Algorithmic mediation for collaborative exploratory search. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’08). ACM, New York, NY, 315--322. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. P. Pirolli and S. Card. 2005. The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. In Proceedings of the International Conference on Intelligence Analysis. 2--4.Google ScholarGoogle Scholar
  53. Tuukka Ruotsalo, Giulio Jacucci, Petri Myllymäki, and Samuel Kaski. 2015. Interactive intent modeling: Information discovery beyond search. Communications of the ACM 58, 1 (2015), 86--92. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. T. Ruotsalo, J. Peltonen, M. Eugster, D. Głowacka, K. Konyushkova, K. Athukorala, I. Kosunen, et al. 2013. Directing exploratory search with interactive intent modeling. In Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (CIKM’13). 1759--1764. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. H. Saito and K. Miwa. 2001. A cognitive study of information seeking processes in the WWW: The effects of searcher’s knowledge and experience. In Proceedings of the 2nd International Conference on Web Information Systems Engineering. IEEE, Los Alamitos, CA, 321--327. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. G. Shani and N. Tractinsky. 2013. Displaying relevance scores for search results. In Proceedings of the 36th International Conference on Research and Development in Information Retrieval (SIGIR’13). ACM, New York, NY, 901--904. Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. B. Shneiderman, D. Byrd, and W. B. Croft. 1998. Sorting out searching: A user-interface framework for text searches. Communications of the ACM 41, 4 (1998), 95--98. Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. P. E. Shrout and N. Bolger. 2002. Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods 7, 4 (2002), 422--445.Google ScholarGoogle ScholarCross RefCross Ref
  59. Karen Sparck Jones. 1972. A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation 28, 1 (1972), 11--21.Google ScholarGoogle ScholarCross RefCross Ref
  60. R. W. White, B. Kules, S. M. Drucker, et al. 2006. Special Issue: Supporting exploratory search: Introduction. Communications of the ACM 49, 4 (2006), 36--39. Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. M. L. Wilson, B. Kules, M. C. Schraefel, and B. Shneiderman. 2010. From keyword search to exploration: Designing future search interfaces for the web. Foundations and Trends in Web Science 2, 1 (2010), 1--97. Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. Ronghui Xu. 2003. Measuring explained variation in linear mixed effects models. Statistics in Medicine 22, 22 (2003), 3527--3541.Google ScholarGoogle ScholarCross RefCross Ref
  63. K.-P. Yee, K. Swearingen, K. Li, and M. A. Hearst. 2003. Faceted metadata for image search and browsing. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’03). ACM, New York, NY, 401--408. Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. Zhen Yue and Daqing He. 2018. Collaborative Information Search. Springer International Publishing, Cham, Switzerland, 108--141.Google ScholarGoogle Scholar

Index Terms

  1. A Roadmap to User-Controllable Social Exploratory Search

          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

          Full Access

          • Published in

            cover image ACM Transactions on Interactive Intelligent Systems
            ACM Transactions on Interactive Intelligent Systems  Volume 10, Issue 1
            Special Issue on IUI 2018
            March 2020
            347 pages
            ISSN:2160-6455
            EISSN:2160-6463
            DOI:10.1145/3352585
            Issue’s Table of Contents

            Copyright © 2019 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: 30 August 2019
            • Accepted: 1 October 2018
            • Revised: 1 September 2018
            • Received: 1 May 2018
            Published in tiis Volume 10, Issue 1

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article
            • Research
            • Refereed

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          HTML Format

          View this article in HTML Format .

          View HTML Format