Abstract
Implicit relevance feedback has received wide attention recently, as a means to capture the search context in improving search accuracy. However, implicit feedback is usually not available to public or even research communities at large for reasons like being a potential threat to privacy of web users. This makes it difficult to experiment and evaluate web search related research and especially web search personalization algorithms. Given these problems, we are motivated towards an artificial way of creating user relevance feedback, based on insights from query log analysis. We call this simulated feedback. We believe that simulated feedback can be immensely beneficial to web search engine and personalization research communities by greatly reducing efforts involved in collecting user feedback. The benefits from “Simulated feedback” are—It is easy to obtain and also the process of obtaining the feedback data is repeatable and customizable. In this chapter, we describe a simple yet effective approach for creating simulated feedback. We evaluated our system using clickthrough data of a set of real world users and achieved 65% accuracy in generating click-through data of those users.
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Notes
- 1.
In Fig. 10.2, we have normalized the clicks statistics with the number of clicks for top ranked document. So, the click-ratio for the top ranked document will be 1.
References
Belkin, N.J.: Reading time, scrolling and interaction: Exploring implicit sources of user preferences for relevance feedback during interactive information retrieval. In: Proceedings of the 24th Annual International Conference on Research and Development in Information Retrieval, SIGIR, pp. 408–409 (2001)
Brown, D.: Implicit interest indicators. In Intelligent User Interfaces, pp. 33–40 (2001)
Cao, L.: In-depth behavior understanding and use: the behavior informatics approach. Inf. Sci. 180, 3067–3085 (2010)
Cho, J.: Automatic identification of user interest for personalized search. In: Proceedings of WWW (2006)
Dennis, S.: Handbook of Latent Semantic Analysis. Lawrence Erlbaum Associates, Mahwah (2007)
Gay, G.: Eyetracking analysis of user behavior in www search. In: Conference on Research and Development in Information Retrieval, SIGIR (2004)
Harman, D.: Towards interactive query expansion. In: The 11th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 321–331 (1988)
Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133–142 (2002)
Joachims, T.: Evaluating the robustness of learning from implicit feedback. In: ICML Workshop on Learning in Web Search (2005)
Pan, B.: Accurately interpreting clickthrough data as implicit feedback. In: Proceedings of 28th Conference on Research and Development in Information Retrieval, SIGIR (2005)
Ragno, R.: Learning user interaction models for predicting web search result preferences. In: Proceedings of 29th Conference on Research and Development in Information Retrieval, SIGIR, pp. 3–10 (2006)
van Rijsbergen, C.J.: Evaluating implicit feedback models using searcher simulations. In: ACM Transactions on Information Systems, ACM TOIS, pp. 325–361 (2005)
Rocchio, J.J.: The smart retrieval system experiments in automatic document processing (1999)
Tan, B.: Implicit user modeling for personalized search. ACM Trans. Inform. Sys. (2005)
White, T.: Evaluating implicit measures to improve web search. ACM Trans. Inf. Sys. 23, 147–168 (2005)
Yoshikawa, M.: Adaptive web search based on user profile constructed without any effort from users. In: Proceedings of WWW, pp. 675–684 (2004)
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© 2012 Springer-Verlag London
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Kumar M, N., Varma, V. (2012). An Introduction to Prognostic Search. In: Cao, L., Yu, P. (eds) Behavior Computing. Springer, London. https://doi.org/10.1007/978-1-4471-2969-1_10
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DOI: https://doi.org/10.1007/978-1-4471-2969-1_10
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