Abstract
Personalized search has recently attracted increasing attention. This paper focuses on utilizing click-through data to personalize the web search results, from a novel perspective based on subspace projection. Specifically, we represent a user profile as a vector subspace spanned by a basis generated from a word-correlation matrix, which is able to capture the dependencies between words in the “satisfied click” (SAT Click) documents. A personalized score for each document in the original result list returned by a search engine is computed by projecting the document (represented as a vector or another word-correlation subspace) onto the user profile subspace. The personalized scores are then used to re-rank the documents through the Borda’ ranking fusion method. Empirical evaluation is carried out on a real user log data set collected from a prominent search engine (Bing). Experimental results demonstrate the effectiveness of our methods, especially for the queries with high click entropy.
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References
Melucci, M.: A Basis for Information Retrieval in Context. ACM Transactions on Information Systems (TOIS) 26(3), 14 (2008)
Dou, Z., Song, R., Wen, J.R.: A large-scale Evaluation and Analysis of Personalized Search Strategies. In: WWW, pp. 581–590 (2007)
Teevan, J., Dumais, S.T., Horvitz, E.: Potential for Personalization. ACM Transactions on Computer-Human Interaction (TOCHI) 17(1), 4 (2010)
Bennett, P.N., Radlinski, F., White, R.W., Yilmaz, E.: Inferring and using Location Metadata to Personalize Web Search. In: SIGIR, pp. 135–144 (2011)
Sontag, D., Collins-Thompson, K., Bennett, P.N., White, R.W., Dumais, S., Billerbeck, B.: Probabilistic Models for Personalizing Web Search. In: WSDM, pp. 433–442 (2012)
White, R.W., Bennett, P.N., Dumais, S.T.: Predicting Short-term Interests using Activity-based Search Context. In: CIKM, pp. 1009–1018 (2010)
Xiang, B., Jiang, D., Pei, J., Sun, X., Chen, E., Li, H.: Context-aware Ranking in Web Search. In: SIGIR, pp. 451–458 (2010)
Agichtein, E., Brill, E., Dumais, S.: Improving Web Search Ranking by Incorporating User Behavior Information. In: SIGIR, pp. 19–26 (2006)
Zhang, S., Dong, N.: An Effective Combination of Different Order N-grams. In: Proceedings of O-COCOSDA, pp. 251–256 (2003)
Bennett, P.N., White, R.W., Chu, W., Dumais, S.T., Bailey, P., Borisyuk, F., Cui, X.: Modeling the Impact of Short-and Long-term Behavior on Search Personalization. In: SIGIR, pp. 185–194 (2012)
Golub, G., Loan, C.V.: Matrix Computation, 2nd edn. The Johns Hopkins University Press, Baltimore (1989)
Xu, S., Bao, S., Fei, B., Su, Z., Yu, Y.: Exploring Folksonomy for Personalized Search. In: SIGIR, pp. 155–162. ACM (2008)
Sun, J.T., Zeng, H.J., Liu, H., Lu, Y., Chen, Z.: CubeSVD: A Novel approach to Personalized Web Search, pp. 382–390 (2005)
Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank Aggregation Methods for the Web, pp. 613–622 (2001)
Melucci, M.: Context Modeling and Discovery using Vector Space Bases. In: CIKM, pp. 808–815 (2005)
Melucci, M., White, R.W.: Utilizing a Geometry of Context for Enhanced Implicit Feedback. In: CIKM, pp. 273–282 (2007)
Van, R., Cornelis, J.: The Geometry of Information Retrieval. The Cambridge University Press (2004)
Porter, M.F.: An Algorithm for Suffix Stripping, Program 14(3), 130–137 (1980)
Zhai, C., Lafferty, J.: A Study of Smoothing Methods for Language Models applied to ad hoc Information Retrieval. In: SIGIR, pp. 334–342 (2001)
Collins-Thompson, K., Bennett, P.N., White, R.W., de la Chica, S., Sontag, D.: Personalizing Web Search Results by Reading Level. In: CIKM, pp. 403–412 (2011)
Nanas, N., Vavalis, M., De Roeck, A.N.: A Network-based Model for High-dimensional Information Filtering. In: SIGIR, pp. 202–209 (2010)
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Li, J., Song, D., Zhang, P., Wen, JR., Dou, Z. (2014). Personalizing Web Search Results Based on Subspace Projection. In: Jaafar, A., et al. Information Retrieval Technology. AIRS 2014. Lecture Notes in Computer Science, vol 8870. Springer, Cham. https://doi.org/10.1007/978-3-319-12844-3_14
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DOI: https://doi.org/10.1007/978-3-319-12844-3_14
Publisher Name: Springer, Cham
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