Abstract
Web search that utilizes social tagging data suffers from an extreme example of the vocabulary mismatch problem encountered in traditional Information Retrieval (IR). This is due to the personalized, unrestricted vocabulary that users choose to describe and tag each resource. Previous research has proposed the utilization of query expansion to deal with search in this rather complicated space. However, non-personalized approaches based on relevance feedback and personalized approaches based on co-occurrence statistics have only demonstrated limited improvements. This paper proposes an Iterative Personalized Query Expansion Algorithm for Web Search (iPAW), which is based on individual user profiles mined from the annotations and resources the user has marked. The method also incorporates a user model constructed from a co-occurrence matrix and from a Tag-Topic model where annotations and web documents are connected in a latent graph. The experimental results suggest that the proposed personalized query expansion method can produce better results than both the classical non-personalized search approach and other personalized query expansion methods. An “adaptivity factor” was further investigated to adjust the level of personalization.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agichtein, E., Brill, E., Dumais, S.: Improving web search ranking by incorporating user behavior information. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 19–26. ACM, Seattle (2006)
Amati, G., Rijsbergen, C.J.V.: Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Trans. Inf. Syst. 20(4), 357–389 (2002)
Baeza-Yates, R.A., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc. (1999)
Bender, M., Crecelius, T., Kacimi, M., Michel, S., Neumann, T., Parreira, J.X., Schenkel, R., Weikum, G.: Exploiting social relations for query expansion and result ranking. In: IEEE 24th International Conference on Data Engineering Workshop, ICDEW 2008, April 7-12, pp. 501–506 (2008)
Bertier, M., Guerraoui, R., Leroy, V., Kermarrec, A.-M.: Toward personalized query expansion. In: Proceedings of the Second ACM EuroSys Workshop on Social Network Systems, pp. 7–12. ACM, Nuremberg (2009)
Biancalana, C., Micarelli, A.: Social Tagging in Query Expansion: A New Way for Personalized Web Search. In: Proceedings of the 2009 International Conference on Computational Science and Engineering, vol. 04, pp. 1060–1065. IEEE Computer Society (2009)
Carmel, D., Zwerdling, N., Guy, I., Ofek-Koifman, S., Har’el, N., Ronen, I., Uziel, E., Yogev, S., Chernov, S.: Personalized social search based on the user’s social network. In: Proceeding of the 18th ACM Conference on Information and Knowledge Management, pp. 1227–1236. ACM, Hong Kong (2009)
Chirita, P.-A., Firan, C.S., Nejdl, W.: Personalized query expansion for the web. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 7–14. ACM, Amsterdam (2007)
Cronen-Townsend, S., Zhou, Y., Croft, W.B.: Predicting query performance. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 299–306. ACM, Tampere (2002)
Cui, H., Wen, J.-R., Nie, J.-Y., Ma, W.-Y.: Query Expansion by Mining User Logs. IEEE Trans. on Knowl. and Data Eng. 15(4), 829–839 (2003)
Harter, S.P.: Online information retrieval: concepts, principles, and techniques. Academic Press Professional, Inc. (1986)
Joachims, T., Granka, L., Pan, B., Hembrooke, H., Gay, G.: Accurately interpreting clickthrough data as implicit feedback. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 154–161. ACM, Salvador (2005)
Qiu, Y., Frei, H.-P.: Concept based query expansion. In: Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 160–169. ACM, Pittsburgh (1993)
Shrager, J., Hogg, T., Huberman, B.A.: Observation of phase transitions in spreading activation network. Science 236, 1092–1093 (1987)
Steyvers, M., Smyth, P., Rosen-Zvi, M., Griffiths, T.: Probabilistic author-topic models for information discovery. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 306–315. ACM, Seattle (2004)
Voorhees, E.M.: Query expansion using lexical-semantic relations. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 61–69. Springer-Verlag New York, Inc., Dublin (1994)
Xu, S., Bao, S., Fei, B., Su, Z., Yu, Y.: Exploring folksonomy for personalized search. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 155–162. ACM, Singapore (2008)
Zhang, Y., Koren, J.: Efficient bayesian hierarchical user modeling for recommendation system. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 47–54. ACM, Amsterdam (2007)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Scholkopf, B.: Learning with local and global consistency. In: Advances in Neural Information Processing Systems, vol. 16, pp. 321–328 (2004)
Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using Gaussian fields and harmonic functions. In: The 20th International Conference on Machine Learning (ICML 2003), pp. 912–919 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, D., Lawless, S., Wade, V. (2012). Web Search Personalization Using Social Data. In: Zaphiris, P., Buchanan, G., Rasmussen, E., Loizides, F. (eds) Theory and Practice of Digital Libraries. TPDL 2012. Lecture Notes in Computer Science, vol 7489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33290-6_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-33290-6_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33289-0
Online ISBN: 978-3-642-33290-6
eBook Packages: Computer ScienceComputer Science (R0)