Abstract
To best exploit the context information for meaningful hints to the user’s intent, this paper proposes an adaptive weighting approach to improve the current context-sensitive retrieval model. The potential for adaptability is first investigated as the performance gap between the current context-sensitive models with a fixed form weight and those with adaptive weights for contextual information. Then the proper context weight is predicated according to the relation strength between the query and its context. The experimental results on a public available dataset indicate that the proposed approach outperforms three baseline methods.
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References
Shen, X., Tan, B., Zhai, C.: Context-Sensitive Information Retrieval Using Implicit Feedback. In: 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 43–50. ACM Press, New York (2005)
Xiang, B., Jiang, D., Pei, J., Sun, X., Chen, E., Li, H.: Context-Aware Ranking in Web Search. In: 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 451–458. ACM Press, New York (2010)
Cao, H., Hu, D.H., Shen, D., Jiang, D., Sun, J.T., Chen, E., Yang, Q.: Context-Aware Query Classification. In: 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3–10. ACM Press, New York (2009)
Cao, H., Jiang, D., Pei, J., Chen, E., Li, H.: Towards Context-Aware Search by Learning a Very Large Variable Length Hidden Markov Model from Search Logs. In: 18th International Conference on World Wide Web, pp. 191–200. ACM Press, New York (2009)
Cao, H., Jiang, D., Pei, J., He, Q., Liao, Z., Chen, E., Li, H.: Context-Aware Query Suggestion by Mining Clickthrough and Session Data. In: 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 875–883. ACM Press, New York (2008)
Kotov, A., Bennett, P.N., White, R.W., Dumains, S., Teevan, J.: Modeling and Analysis of Cross-Session Search Task. In: 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 5–14. ACM Press, New York (2011)
Luxenburger, J., Elbassuoni, S., Weikum, G.: Matching Task Profiles and User Needs in Personalized Web Search. In: 17th ACM Conference on Information and Knowledge Management, pp. 689–698. ACM Press, New York (2008)
Teevan, J., Dumais, S., Horvitz, E.: Personalizing Search via Automated Analysis of Interests and Activities. In: 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 449–456. ACM Press, New York (2005)
Teevan, J., Dumais, S., Horvitz, E.: Potential for Personalization. Transactions on Computer-Human Interaction 17(1) (2010)
Teevan, J., Dumais, S., Liebling, D.: To Personalized or Not to Personalize: Modeling Queries with Variation in User Intent. In: 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 163–170. ACM Press, New York (2008)
Pitkow, J., Schütze, H., Cass, T., Cooley, R., Turnbull, D., Edmonds, A., Adar, E., Breuel, T.: Personalized Search. Communications of the ACM 45(9), 50–55 (2002)
Shen, X., Tan, B., Zhai, C.: Implicit User Modeling for Personalized Search. In: 14th ACM International Conference on Information and Knowledge Management, pp. 824–831. ACM Press, New York (2005)
Pitkow, J., Schütze, H., Cass, T., Cooley, R., Turnbull, D., Edmonds, A., Adar, E., Breuel, T.: Personalized Search. Communications of the ACM 45(9), 50–55 (2002)
Teevan, J., Dumais, S., Horvitz, E.: Personalizing Search via Automated Analysis of Interests and Activities. In: 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 449–456. ACM Press, New York (2005)
Teevan, J., Dumais, S., Horvitz, E.: Potential for Personalization. Transactions on Computer-Human Interaction 17(1) (2010)
Xiang, B., Jiang, D., Pei, J., Sun, X., Chen, E., Li, H.: Context-Aware Ranking in Web Search. In: 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 451–458. ACM Press, New York (2010)
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Wang, X., Yang, M., Qi, H., Li, S., Zhao, T. (2012). Adaptive Weighting Approach to Context-Sensitive Retrieval Model. In: Hou, Y., Nie, JY., Sun, L., Wang, B., Zhang, P. (eds) Information Retrieval Technology. AIRS 2012. Lecture Notes in Computer Science, vol 7675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35341-3_37
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DOI: https://doi.org/10.1007/978-3-642-35341-3_37
Publisher Name: Springer, Berlin, Heidelberg
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