Web Search Personalization Using Social Data
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.
KeywordsPersonalized Web Search Query Expansion Social Data Tag- Topic Model Graph Algorithm
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- 3.Baeza-Yates, R.A., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc. (1999)Google Scholar
- 4.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)Google Scholar
- 6.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)Google Scholar
- 7.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)CrossRefGoogle Scholar
- 11.Harter, S.P.: Online information retrieval: concepts, principles, and techniques. Academic Press Professional, Inc. (1986)Google Scholar
- 12.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)Google Scholar
- 15.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)Google Scholar
- 16.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)Google Scholar
- 19.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)Google Scholar
- 20.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)Google Scholar