Abstract
This paper presents a novel probabilistic framework for broadening the notion of context in web search query recommendation systems. In the relevant literature, query suggestion is typically conducted based on past user actions of the current session, mostly related to query submission. Our proposed framework regards user context in a broader way, consisting of a series of further parameters that express it more thoroughly, such as spatial and temporal ones. Therefore, query recommendation is performed herein by considering the appropriateness of each candidate query suggestion, given this broadened context. Experimental evaluation showed that our proposed framework, utilizing spatiotemporal contextual features, is capable to increase query recommendation performance, compared to state-of-art methods such as co-occurence, adjacency and Variable-length Markov Models (VMM). Due to its generic nature, our framework can operate on the basis of further features expressing the user context than the ones studied in the present work, e.g. affect-related, toward further advancing web search query recommendation.
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
Wen, J.-R., Nie, J.-Y., Zhang, H.-J.: Clustering user queries of a search engine. In: Proceedings of the 10th International Conference on World Wide Web (2001)
Cui, H., Wen, J.-R., Nie, J.-Y., Ma, W.-Y.: Probabilistic query expansion using query logs. In: Proceedings of the 11th International Conference on World Wide Web (2002)
He, Q., Jiang, D., Liao, Z., Hoi, S.C.H., Kuiyu, C., EePeng, L., et al.: Web Query Recommendation via Sequential Query Prediction. Paper presented at the IEEE 25th International Conference on Data Engineering, ICDE, March 29-April 2 (2009)
Qiu, F., Cho, J.: Automatic identification of user interest for personalized search. In: Proc. Fifteenth Int’l Conf. on World Wide Web (WWW 2006), pp. 727–736. ACM, New York (2006)
Kato, M., Sakai, T., Tanaka, K.: When do people use query suggestion? A query suggestion log analysis. Information Retrieval 16(6), 725–746
Huang, C.-K., Chien, L.-F., Oyang, Y.-J.: Relevant term suggestion in interactive web search based on contextual information in query session logs. Journal of the American Society for Information Science and Technology 54(7), 638–649 (2003)
Jones, R., Rey, B., Madani, O., Greiner, W.: Generating query substitutions. In: ACM WWW, pp. 387–396 (2006)
Meij, E., Bron, M., Hollink, L., Huurnink, B., de Rijke, M.: Learning Semantic Query Suggestions. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 424–440. Springer, Heidelberg (2009)
Cao, H., Jiang, D., Pei, J., He, Q., Liao, Z., Chen, E., et al.: Context-aware query suggestion by mining click-through and session data. Paper Presented at the Proceedings of the 14th ACM SIGKDD Int’l. Conf. on Knowledge Discovery and Data Mining (2008)
Mei, Q., Church, K.: Entropy of search logs: how hard is search? with personalization? with backoff? Paper Presented at the Proceedings of the 2008 International Conference on Web Search and Data Mining (2008)
Bennett, P.N., Radlinski, F., White, R.W., Yilmaz, E.: Inferring and using location metadata to personalize web search. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (2011)
Silverstein, C., Marais, H., Henzinger, M., Moricz, M.: Analysis of a very large web search engine query log. SIGIR Forum 33(1), 6–12 (1999)
Adomavicius, G., Tuzhilin, A.: Context-Aware Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, US (2011)
Pass, G., Chowdhury, A., Torgeson, C.: A picture of search. In: InfoScale 2006, p. 1 (2006)
Ron, D., Singer, Y., Tishby, N.: Learning probabilistic automata with variable memory length. In: COLT, pp. 35–46 (1994)
Zhang, H.: The optimality of naive Bayes. In: Proceedings of the 17th International FLAIRS Conference (FLAIRS2004). AAAI Press (2004)
Liao, Z., Jiang, D., Pei, J., Huang, Y., Chen, E., Cao, H., Li, H.: A vlHMM approach to context-aware search. ACM Trans. Web 7(4), Article 22 (2013)
Qumsiyeh, R., Ng, Y.K.: Assisting web search using query suggestion based on word similarity measure and query modification patterns. In: World Wide Web, pp. 1–20 (2014)
Goyal, P., Mehala, N., Bansal, A.: A robust approach for finding conceptually related queries using feature selection and tripartite graph structure. Journal of Information Science 39(5), 575–592 (2013)
Miyanishi, T., Sakai, T.: Time-aware structured query suggestion. In: Proc. of the 36th Int’l ACM SIGIR Conf. (SIGIR 2013) (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Giakoumis, D., Tzovaras, D. (2014). A Novel Probabilistic Framework to Broaden the Context in Query Recommendation Systems. In: Likas, A., Blekas, K., Kalles, D. (eds) Artificial Intelligence: Methods and Applications. SETN 2014. Lecture Notes in Computer Science(), vol 8445. Springer, Cham. https://doi.org/10.1007/978-3-319-07064-3_51
Download citation
DOI: https://doi.org/10.1007/978-3-319-07064-3_51
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07063-6
Online ISBN: 978-3-319-07064-3
eBook Packages: Computer ScienceComputer Science (R0)