İlyas ÇİÇEKLİ, Samira KARIMI MANSOUB, Gönenç ERCAN

Selective personalization and group profiles for improved web search personalization

Personalization is a common technique used in Web search engines to improve the effectiveness of retrieval.While personalizing some queries yields significant improvements in user experience by providing a ranking in line withthe user preferences, it fails to improve or even degrades the effectiveness for less ambiguous queries. A potentialpersonalization metric could improve search engines by selectively applying personalization. One such measure, clickentropy uses the query history and the clicked documents for the query, which might be sparse for some queries. Inthis article, the topic entropy measure is improved by integrating the user distribution into the metric, robust to thesparsity problem. Furthermore, a topic model-based ranking for the personalization method is proposed using groupeduser profiles. Experiments reveal that the proposed potential prediction method correlates with human query ambiguityjudgments and the group profile-based ranking method improves the mean reciprocal rank by 8%.

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[1] Teevan J, Dumais S, Horvitz E. Beyond the commons: investigating the value of personalizing web search. In: Proceedings of the Workshop on New Technologies for Personalized Information Access; Edinburgh, UK; 2005. pp. 84-92.
[2] Dou Z, Song R, Wen J. A large-scale evaluation and analysis of personalized search strategies. In: Proceedings of the 16th International Conference on World Wide Web; Banff, Canada; 2007. pp. 581-590.
[3] Yano Y, Tagami Y, Tajima A. Quantifying query ambiguity with topic distributions. In: Proceedings of the 25th ACM International Conference on Information and Knowledge Management; Indianapolis, IN, USA; 2016. pp. 1877-1880.
[4] Harvey M, Crestani F, Carman M. Building user profiles from topic models for personalised search. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management; San Francisco, CA, USA; 2013. pp. 2309-2314.
[5] Carman M, Crestani F, Harvey M, Baillie M. Towards query log based personalization using topic models. In: Proceedings of the 19th ACM International conference on Information and Knowledge Management; Toronto, Canada; 2010. pp. 1849-1852.
[6] Teevan J, Dumais S, Horvitz E. Potential for personalization. In: ACM Transactions on Computer-Human Interaction; 2010. pp. 1-31.
[7] Teevan J, Dumais S, Liebling D. To personalize or not to personalize: modeling queries with variation in user intent. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; Singapore; 2008. pp. 163-170.
[8] Cronen-Townsend S, Zhou Y, Croft W. Predicting query performance. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; Tampere, Finland; 2002. pp. 299-306.
[9] Wang Y, Agichtein E. Query ambiguity revisited: clickthrough measures for distinguishing informational and ambiguous queries. In: HLT ’10 Human Language Technologies: 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics; 2010. pp. 361-364.
[10] Song R, Luo Z, Wen J, Yu Y, Hon H. Identifying ambiguous queries in web search. In: Proceedings of the 16th International Conference on World Wide Web; Banff, Canada; 2007. pp. 1169-1170.
[11] Matthijs N, Radlinski F. Personalizing web search using long term browsing history. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining; Hong Kong; 2011. pp. 25-34.
[12] Sieg A, Mobasher B, Burke R. Web search personalization with ontological user profiles. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management; Lisbon, Portugal; 2007. pp. 525-534.
[13] Chirita A, Nejdl W, Paiu R, Kohlschütter C. Using ODP metadata to personalize search. In: Proceedings of the 28th Annual International ACM Sigir Conference on Research And Development in Information Retrieval; Salvador, Brazil; 2005. pp. 178-185.
[14] Karimi S, Abri R. Improvement of semantic search results with providing an updatable dynamic user model. International Journal of Computer Applications; 2016; 155 (4): 7-14. doi: 10.5120/ijca2016912285
[15] Vu T, Willis A, Tran S, Song D. Temporal latent topic user profiles for search personalisation. In: ECIR 37th European Conference on IR Research; Vienna, Austria; 2015. pp. 605-616.
[16] Vu T, Willis A, Kruschwitz U, Song D. Personalised query suggestion for intranet search with temporal user profiling. In: Proceedings of the 2017 Conference Human Information Interaction and Retrieval; Oslo, Norway; 2017. pp. 265-268.
[17] Bennett P, White R, Chu W, Dumais S, Bailey P et al. Modeling the impact of short- and long-term behavior on search personalization. In: Proceedings of the 35th international ACM SIGIR Conference on Research and Development in Information Retrieval; Portland, OR, USA; 2012. pp. 185-194.
[18] Hofmann T. Probabilistic latent semantic analysis. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters; 2010. pp. 1167-1175.
[19] Wei S, Yu Z, Ting L, Sheng L. Bridging topic modeling and personalized search. In: Proceedings of the Fifteenth conference on Uncertainty in Artificial Intelligence: Posters; Stockholm, Sweden; 1999. pp. 289-296.
[20] Shao M, Qin L. Text similarity computing based on LDA topic model and word co-occurrence. In: Proceedings of 2nd International Conference on Software Engineering, Knowledge Engineering and Information Engineering: Posters; Singapore; 2014.
[21] Momtazi S, Lindenberg F. Generating query suggestions by exploiting latent semantics in query logs. Journal of Information Science; 2016; 42 (4): 437-448. doi: 10.1177/0165551515594723
[22] Amer N, Mulhem P, Géry M. Toward word embedding for personalized information retrieval. Neu-IR: SIGIR 2016 Workshop on Neural Information Retrieval; Pisa, Italy; 2016.
[23] Vu T, Nguyen D, Dat Q, John M, Song D et al. Search personalization with embeddings. European Conference on Information Retrieval; Aberdeen, UK; 2017. pp. 598-604.
[24] Manning C, Raghavan P, Schütze H. Introduction to Information Retrieval. Cambridge, MA, USA: Cambridge University Press, 2008.
[25] Vu T, Willis A, Song D. Modelling time-aware search tasks for search personalisation. In: Proceedings of the 24th International Conference on World Wide Web; Florence, Italy; 2015. pp. 131-132.