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Intent and its discontents: the user at the wheel of the online video search engine

Published:29 October 2012Publication History

ABSTRACT

We embrace the position of the user in the driver's seat of the video search engine by proposing a principled framework for multimedia retrieval that moves beyond what users are searching for also to encompass why they search. This 'why' is understood as the reason, purpose or immediate goal behind a user information need, which we identify as the underlying user 'intent'. Breaking information needs down into a topical dimension representing 'what' and an intent dimension representing 'why' will allow online video search engines to provide users with more satisfying search results. Until now, research on intent has remained small scale, limited by the lack of a systematic method for arriving at possible dimensions of user intent that provide productive areas of inquiry for multimedia research. We demonstrate how mining information from user descriptions of video information needs on the social Web makes it possible to identify useful intent categories for online video search and carry out validation experiments showing that these categories display enough invariance to be successfully modeled by a video search engine. In a final experiment, we demonstrate the potential for these categories to improve video retrieval with a large user study confirming that users associate salient differences within topically homogenous video search engine results lists with these intent categories. This reveals the potential to refine video results list using user intent.

References

  1. Baeza-Yates, R., Caldern-Benavides, L. and Gonzlez-Caro, C. The intention behind web queries. In String Proc. and IR (2006). Springer-Verlag, 98--109. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Broder, A. A taxonomy of web search. SIGIR Forum, 36, 2, 2002, 3--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Cao, H., Hu, D. H., Shen, D., Jiang, D., Sun, J.-T., Chen, E. and Yang, Q. Context-aware query classification. In SIGIR (2009). ACM, 3--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Chang, S.-F. Content based multimedia retrieval: lessons learned from two decades of research. In MM (2011). ACM, 1--2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Datta, R., Joshi, D., Li, J. and Wang, J. Z. Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Surv., 40, 2, 2008, 1--60. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Fidel, R. The image retrieval task: implications for the design and evaluation of image databases. New Review of Hyper-media and Multimedia, 3, 1, 1997, 181--199.Google ScholarGoogle ScholarCross RefCross Ref
  7. Fox, S., Karnawat, K., Mydland, M., Dumais, S. and White, T. Evaluating implicit measures to improve web search. ACM Trans. Inf. Syst., 23, 2, 2005, 147--168. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Guo, J., Cheng, X., Xu, G. and Zhu, X. Intent-aware query similarity. In CIKM (2011). ACM, 259--268. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Hsu, W. H., Kennedy, L. S. and Chang, S.-F. Reranking Methods for Visual Search. IEEE MultiMedia, 14, 3, 2007, 14--22. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Ionescu, B., Seyerlehner, K., Rasche, C., Vertan, C. and Lambert, P. An audio-visual approach to web video categori-zation. MTAP 2012, 1--26.Google ScholarGoogle Scholar
  11. Jansen, B. J., Spink, A. and Pedersen, J. The effect of special-ized multimedia collections on web searching. J. Web Eng., 3, 3, 2004, 182--199. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Joachims, T. Text categorization with support vector machines: learning with many relevant features. Springer Verlag, Heidelberg, DE, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Kennedy, L., Shih-Fu, C. and Natsev, A. Query-Adaptive Fusion for Multimodal Search. IEEE, 96, 4, 2008, 567--588.Google ScholarGoogle Scholar
  14. Kofler, C. and Lux, M. Dynamic presentation adaptation based on user intent classification. In MM (2009). ACM, 1117--1118. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Lamel, L. and Gauvain, J.-L. Speech Processing for Audio Indexing. In Advances in NLP (2008). Springer, 4--15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Larson, M., Eskevich, M., Ordelman, R., Kofler, C., Schmiedeke, S. and Jones, G. Overview of MediaEval 2011 Rich Speech Retrieval Task and Genre Tagging Task. 2011.Google ScholarGoogle Scholar
  17. Larson, M., Soleymani, M., Serdyukov, P., Rudinac, S., Wartena, C., Murdock, V., Friedland, G., Ordelman, R. and Jones, G. Automatic tagging and geotagging in video collec-tions and communities. In ICMR (2011). ACM, 1--8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Lux, M., Kofler, C. and Marques, O. A classification scheme for user intentions in image search. In CHI (2010). ACM, 3913--3918. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Mezaris, V., Gidaros, S., Kasper, W., Steffen, J., Ordelman, R., Huijbregts, M., Jong, F. d., Kompatsiaris, I. and Strintzis, M. G. A system for the semantic multimodal analysis of news audio-visual content. EURASIP J. Adv. Signal Process, 20102010, 1--16. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Montagnuolo, M. and Messina, A. TV Genre Classification Using Multimodal Information and Multilayer Perceptrons. Springer Berlin / Heidelberg, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Naphade, M., Smith, J. R., Tesic, J., Chang, S.-F., Hsu, W., Kennedy, L., Hauptmann, A. and Curtis, J. Large-Scale Con-cept Ontology for Multimedia. IEEE MultiMedia, 13, 3, 2006, 86--91. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Rose, D. E. and Levinson, D. Understanding user goals in web search. In WWW (2004). ACM, 13--19. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Rugg, G. and McGeorge, P. The sorting techniques: a tutorial paper on card sorts, picture sorts and item sorts Expert Sys-tems, 22, 3, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  24. Santos, R. L. T., Macdonald, C. and Ounis, I. Intent-aware search result diversification. In SIGIR (2011). ACM, 595--604. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Schmiedeke, S., Kelm, P. and Sikora, T. Cross-Modal Cate-gorisation of User-Generated Video Sequences. In ICMR (2012), 1--8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Snoek, C. G. M. and Worring, M. Concept-Based Video Retrieval. Found. Trends Inf. Retr., 2, 4, 2009, 215--322. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Truong, B. T. and Dorai, C. Automatic Genre Identification for Content-Based Video Categorization. In ICPR (2000). IEEE Computer Society, 4230. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Vliegendhart, R., Larson, M. and Pouwelse, J. Discovering User Perceptions of Semantic Similarity in Near-duplicate Multimedia Files. In CrowdSearch (WWW) (2012)Google ScholarGoogle Scholar
  29. Yan, R., Yang, J. and Hauptmann, A. G. Learning query-class dependent weights in automatic video retrieval. In MM (2004). ACM, 548--555. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Yang, L. and Hanjalic, A. Supervised reranking for web image search. In MM (2010). ACM, 183--192. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Zha, Z.-J., Yang, L., Mei, T., Wang, M., Wang, Z., Chua, T.-S. and Hua, X.-S. Visual query suggestion: Towards capturing user intent in internet image search. ACM Trans. Multi-media Comput. Commun. Appl., 6, 3, 2010, 1--19. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Conferences
      MM '12: Proceedings of the 20th ACM international conference on Multimedia
      October 2012
      1584 pages
      ISBN:9781450310895
      DOI:10.1145/2393347

      Copyright © 2012 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 ACM 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]

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      Publication History

      • Published: 29 October 2012

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