Abstract
The performance of search personalisation largely depends on how to build user profiles effectively. Many approaches have been developed to build user profiles using topics discussed in relevant documents, where the topics are usually obtained from human-generated online ontology such as Open Directory Project. The limitation of these approaches is that many documents may not contain the topics covered in the ontology. Moreover, the human-generated topics require expensive manual effort to determine the correct categories for each document. This paper addresses these problems by using Latent Dirichlet Allocation for unsupervised extraction of the topics from documents. With the learned topics, we observe that the search intent and user interests are dynamic, i.e., they change from time to time. In order to evaluate the effectiveness of temporal aspects in personalisation, we apply three typical time scales for building a long-term profile, a daily profile and a session profile. In the experiments, we utilise the profiles to re-rank search results returned by a commercial web search engine. Our experimental results demonstrate that our temporal profiles can significantly improve the ranking quality. The results further show a promising effect of temporal features in correlation with click entropy and query position in a search session.
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
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. ACM (2012)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res., 993–1022 (2003)
Burges, C.J., Ragno, R., Le, Q.V.: Learning to rank with nonsmooth cost functions. In: NIPS, pp. 193–200. MIT Press (2007)
Burges, C.J.C.: From ranknet to lambdarank to lambdamart: An overview. Technical Report MSR-TR-2010-82, Microsoft Research (July 2010)
Chapelle, O., Chang, Y., Liu, T.: Yahoo! learning to rank challenge overview. In: JMLR, pp. 1–24 (2011)
Dou, Z., Song, R., Wen, J.-R.: A large-scale evaluation and analysis of personalized search strategies. In: WWW, pp. 581–590. ACM (2007)
Fox, S., Karnawat, K., Mydland, M., Dumais, S., White, T.: Evaluating implicit measures to improve web search. ACM Trans. Inf. Syst., 147–168 (2005)
Harvey, M., Crestani, F., Carman, M.J.: Building user profiles from topic models for personalised search. In: CIKM, pp. 2309–2314. ACM (2013)
Hassan, A., White, R.W.: Personalized models of search satisfaction. In: CIKM, pp. 2009–2018. ACM (2013)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)
Raman, K., Bennett, P.N., Collins-Thompson, K.: Toward whole-session relevance: Exploring intrinsic diversity in web search. In: SIGIR, pp. 463–472 (2013)
Shokouhi, M., White, R.W., Bennett, P., Radlinski, F.: Fighting search engine amnesia: Reranking repeated results. In: SIGIR, pp. 273–282. ACM (2013)
Song, Y., Shi, X., White, R., Awadallah, A.H.: Context-aware web search abandonment prediction. In: SIGIR, pp. 93–102. ACM (2014)
Teevan, J., Dumais, S.T., Horvitz, E.: Personalizing search via automated analysis of interests and activities. In: SIGIR, pp. 449–456. ACM (2005)
Teevan, J., Morris, M.R., Bush, S.: Discovering and using groups to improve personalized search. In: WSDM, pp. 15–24. ACM (2009)
Vu, T.T., Song, D., Willis, A., Tran, S.N., Li, J.: Improving search personalisation with dynamic group formation. In: SIGIR, pp. 951–954. ACM (2014)
Wang, H., Song, Y., Chang, M.-W., He, X., Hassan, A., White, R.W.: Modeling action-level satisfaction for search task satisfaction prediction. In: SIGIR, pp. 123–132. ACM (2014)
White, R.W., Bennett, P.N., Dumais, S.T.: Predicting short-term interests using activity-based search context. In: CIKM, pp. 1009–1018. ACM (2010)
White, R.W., Chu, W., Hassan, A., He, X., Song, Y., Wang, H.: Enhancing Personalized Search by Mining and Modeling Task Behavior. In: WWW, pp. 1411–1420. ACM (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Vu, T., Willis, A., Tran, S.N., Song, D. (2015). Temporal Latent Topic User Profiles for Search Personalisation. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_67
Download citation
DOI: https://doi.org/10.1007/978-3-319-16354-3_67
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16353-6
Online ISBN: 978-3-319-16354-3
eBook Packages: Computer ScienceComputer Science (R0)