Abstract
A core aspect of search personalization is inferring the user’s search interests. Different approaches may consider different aspects of user information and may have different interpretations of the notion of interest. This may lead to learning disparate characteristics of a user. Although search engines collect a variety of information about their users, the following question remains unanswered: to what extent can personalized search systems harness these information sources to capture multiple views of the user’s interests, and adapt the search accordingly? To answer this question, this paper proposes a hybrid approach for search personalization. The advantage of this approach is that it can flexibly combine multiple sources of user information, and incorporate multiple aspects of user interests. Experimental results demonstrate the effectiveness of the proposed approach for search personalization.
This is a preview of subscription content, log in via an institution to check access.
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
Sanderson, M., Croft, W.B.: The history of information retrieval research. Proceedings of the IEEE 100, 1444–1451 (2012)
Dou, Z., Song, R., et al.: Evaluating the effectiveness of personalized web search. IEEE Transactions on Knowledge and Data Engineering 21(8), 1178–1190 (2009)
Teevan, J., Dumais, S.T., Liebling, D.J.: To personalize or not to personalize: Modeling queries with variation in user intent. In: SIGIR 2008, pp. 163–170. ACM (2008)
Shen, X., Tan, B., Zhai, C.X.: Implicit user modeling for personalized search. In: CIKM 2005, pp. 824-831 (2005)
Micarelli, A., Gasparetti, F., Sciarrone, F., Gauch, S.: Personalized search on the world wide web. In: Brusilovsky, P., Kobsa, A., Nejdl, W., et al. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 195–230. Springer, Heidelberg (2007)
Sun, J.T., Zeng, H.J., Liu, H., et al.: Cubesvd: A novel approach to personalized web search. In: WWW 2005, pp. 382–390. ACM (2005)
Chirita, P.A., Nejdl, W., Paiu, R., et al.: Using ODP metadata to personalize search. In: SIGIR 2005, pp. 178–185. ACM (2005)
Agichtein, E., Brill, E., Dumais, S.: Improving web search ranking by incorporating user behavior information. In: SIGIR 2006, pp. 19–26. ACM (2006)
Bennett, P.N., White, R.W., Chu, W., Dumais, S.T., et al.: Modeling the impact of short-and long-term behavior on search personalization. In: SIGIR 2012, pp. 185–194. ACM (2012)
Kashyap, A., Amini, R., Hristidis, V.: SonetRank: leveraging social networks to personalize search. In: CIKM 2012, pp. 2045–2049. ACM (2012)
Zhou, D., Lawless, S., Wade, V.: Web search personalization using social data. In: Zaphiris, P., Buchanan, G., Rasmussen, E., Loizides, F. (eds.) TPDL 2012. LNCS, vol. 7489, pp. 298–310. Springer, Heidelberg (2012)
Ghorab, M.R., Zhou, D., O’Connor, A., et al.: Personalised information retrieval: survey and classification. User Modeling and User-Adapted Interaction 23(4), 381–443 (2013)
Xu, S., Bao, S., Fei, B., et al.: Exploring folksonomy for personalized search. In: SIGIR 2008, pp. 155–162 (2008)
Badesh, H., Blustein, J.: VDMs for finding and re-finding web search results. In: Proceedings of the 2012 iConference, pp. 419–420. ACM (2012)
Qiu, F., Cho, J.: Automatic identification of user interest for personalized search. In: WWW 2006, 727–736 (2006)
Dwork, C., Kumar, R., et al.: Rank aggregation methods for the web. In: WWW 2001, pp. 613–622. ACM (2001)
Aslam, J.A., Montague, M.: Models for metasearch. In: SIGIR’01, pp. 276–284. ACM (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Xu, Y., Ghorab, M.R., Lawless, S. (2015). Search Personalization via Aggregation of Multidimensional Evidence About User Interests. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2015. Lecture Notes in Computer Science(), vol 9042. Springer, Cham. https://doi.org/10.1007/978-3-319-18117-2_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-18117-2_34
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18116-5
Online ISBN: 978-3-319-18117-2
eBook Packages: Computer ScienceComputer Science (R0)