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Languages: English
Types: Other
Subjects: QA75
User feedback is considered to be a critical element in the information seeking process, especially in relation to relevance assessment. Current feedback techniques determine content relevance with respect to the cognitive and situational levels of interaction that occurs between the user and the retrieval system. However, apart from real-life problems and information objects, users interact with intentions, motivations and feelings, which can be seen as critical aspects of cognition and decision-making. The study presented in this paper serves as a starting point to the exploration of the role of emotions in the information seeking process. Results show that the latter not only interweave with different physiological, psychological and cognitive processes, but also form distinctive patterns, according to specific task, and according to specific user.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] Bailey, P., Craswell, N., and Hawking, D. 2003. Engineering a multi-purpose test collection for web retrieval experiments. Inf. Process. Manage. 39, 853-871.
    • [2] Belkin, N.J., Cool, C., Head, J., Jeng, J., Kelly, D., Lin, S.J., Lobash, L. Park, S.Y., Savage-Knepshield, P., and Sikora, C. Relevance feedback versus Local Context Analysis as term suggestion devices: In Proceedings of the Eighth Text Retrieval Conference TREC8.
    • [3] Bilal, D., and Kirby, J. 2002. Differences and similarities in information seeking: children and adults as web users. Inf. Process. Manage. 38, 5, 649-670.
    • [4] Borlund, P. 2000. Experimental components for the evaluation of interactive information retrieval systems. Journal of Documentation, 56(1), 71-90.
    • [5] Damasio, A. R., 1994. Descartes Error: Emotion, Reason, and the Human Brain. Gosset/Putnam Press, New York.
    • [6] Fasel, B., and Luettin, J., 2003. Automatic facial expression analysis: a survey. In Pattern Recognition. 36, 1, 259-275.
    • [7] Harman, D. 1992. Relevance feedback revisited. In Proceedings of the 15th Annual international ACM SIGIR Conference on Research and Development in information Retrieval. ACM, 1-10.
    • [8] Healey, J., Seger, J., and Picard, R. W., 1999. Quantifying Driver Stress: Developing a System for Collecting and Processing Bio-Metric Signals in Natural Situations.
    • [9] Jaimes, A., and Sebe, N. 2007. Multimodal human-computer interaction: A survey. In Comput. Vis. Image Underst. 108, 1-2, 116-134.
    • [10] Kelly, D., and Belkin, N. J. 2001. Reading time, scrolling and interaction: exploring implicit sources of user preferences for relevance feedback. In Proceedings of the 24th Annual international ACM SIGIR '01. 408-409.
    • [11] Kelly, D., and Belkin, N. J., 2002. A user modeling system for personalized interaction and tailored retrieval in interactive IR. In Proceedings of the American Society for Information Science.
    • [12] Kelly, D., and Teevan, J. 2003. Implicit feedback for inferring user preference: a bibliography. SIGIR Forum 37, 2, 18-28.
    • [13] Kim, K. 2008. Effects of emotion control and task on Web searching behavior. Inf. Process. Manage. 44, 1, 373-385.
    • [14] Koenemann, J., and Belkin, N. J. 1996. A case for interaction: a study of interactive information retrieval behavior and effectiveness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems: Common Ground CHI '96. ACM, 205-212.
    • [15] Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., and Riedl, J. 1997. GroupLens: applying collaborative filtering to Usenet news. Commun. ACM 40, 3, 77-87.
    • [16] Kuhlthau, C. C. 1991. Inside the search process: information seeking from the user's perspective. Journal of American Society for Information Science, 42, 5, 361-371
    • [17] Lavie, T. and Tractinsky, N. 2004. Assessing dimensions of perceived visual aesthetics of web sites. Int. J. Hum.- Comput. Stud. 60, 3, 269-298.
    • [18] Lopatovska, I., and Mokros, H. B. 2008. Willingness to pay and experienced utility as measures of affective value of information objects: Users' accounts. Inf. Process. Manage. 44, 1, 92-104.
    • [19] Mooney, C., Scully, M., Jones, G. J. F., and Smeaton, A. F. 2006. Investigating Biometric Response for Information Retrieval Applications. ECIR 2006: 570-574
    • [20] Morita, M., and Shinoda, Y. 1994. Information filtering based on user behavior analysis and best match text retrieval. In Proceedings of the 17th Annual international ACM SIGIR-94.
    • [21] Nahl, D. 1998. Ethnography of novices' first use of Web search engines: affective control in cognitive processing. Internet Ref. Serv. Q. 3, 2, 51-72.
    • [22] Nahl, D. 2004. Measuring the affective information environment of web searchers. In Proceedings of the American Society for Information Science and Technology. 41, 1, 191-197.
    • [23] Nahl, D. 2005. Affective and Cognitive Information Behavior: Interaction Effects in Internet Use. In Proceedings 68th Annual Meeting of the American Society for Information Science and Technology (ASIST), 42.
    • [24] Nahl, D. and Tenopir, C. 1996. Affective and cognitive searching behavior of novice end-users of a full-text database. J. Am. Soc. Inf. Sci. 47, 4, 276-286.
    • [25] Nichols, D. M. 1997. Implicit Rating and Filtering. In Proceedings of the Fifth DELOS Workshop on Filtering and Collaborative Filtering.
    • [26] Pantic, M., and Rothkrantz, L. J. M., 2003. Toward an Affect-Sensitive Multimodal Human-Computer Interaction. In Proceedings of the IEEE. 91, 9, 1370--1390.
    • [27] Pfister, H. R., and Böhm, G. 2008. The multiplicity of emotions: A framework of emotional functions in decision making. Judgment and Decision Making, 3, 5-17.
    • [28] Picard, R. W., 2001. Building HAL: Computers that Sense, Recognize, and Respond to Human Emotion. In Society of Photo-Optical Instrumentation Engineers.
    • [29] Saracevic, T. 1975. Relevance: A review of and a framework for the thinking on the notion in Information Science. Journal of American Society for information Science, 26, 321-343.
    • [30] Sebe, N., Lew, M. S., Sun, Y., Cohen, I., Gevers, T., and Huang, T. S. 2007. Authentic facial expression analysis. Image Vision Comput. 25, 12, 1856-1863.
    • [31] Scherer, K. R. 2001. Appraisal considered as a process of multi-level sequential checking. Appraisal processes in emotion: Theory, methods, research (pp. 92-120). New York and Oxford: Oxford University Press.
    • [32] Scherer, K. R., 2005. What are emotions? And how can they be measured? In Social Science Information. 44, 4, 695-729.
    • [33] Seo, Y., and Zhang, B. 2000. Learning user's preferences by analyzing Web-browsing behaviors. In Proceedings of the Fourth international Conference on Autonomous Agents 2000. AGENTS '00. ACM, 381-387.
    • [34] Tenopir, C., Wang, P., Zhang, Y., Simmons, B., and Pollard, R. 2008. Academic users' interactions with ScienceDirect in search tasks: Affective and cognitive behaviors. Inf. Process. Manage. 44, 105-121.
    • [35] Valenti, R., Sebe, N., and Gevers, T. 2007. Facial Expression Recognition: A Fully Integrated Approach. 14th International Conference on Image Analysis and Processing Workshops. ICIAPW 2007. 125-130.
    • [36] Wang, P., Hawk, W. P., and Tenopir, C. 2000. Users' interaction with World Wide Web resources: an exploratory study using a holistic approach. Information Processing & Management, 36, 2, 229-251.
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