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Journal of Intelligent Information Systems

Erschienen in:

30.01.2018

Predicting IR personalization performance using pre-retrieval query predictors

verfasst von: Eduardo Vicente-López, Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 3/2018

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Abstract

Although personalization generally improves query performance, it may also occasionally harm how queries perform. If we are able to predict and therefore disable personalization for such situations, overall performance will be higher and users will be more satisfied with personalized systems. We use various state-of-the-art, pre-retrieval query performance predictors and propose several others including user profile information for this purpose. We study the correlations between these predictors and the difference between personalized and original queries. We also use classification and regression techniques to improve the results and finally achieve slightly more than one third of maximum ideal performance. We consider this to be a good starting point within this research line, which will undoubtedly result in further work and improvements.

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https://cran.r-project.org/

We have also used the Spearman and Kendall methods to compute the correlations and similar results were obtained.

http://www.cs.waikato.ac.nz/ml/weka/

so that the tendency of the system is to personalize most of the queries.

Amati, G., Carpineto, C., Romano, G. (2004). Query difficulty, robustness, and selective application of query expansion. In European conference on information retrieval (pp. 127–137): Springer.

Aslam, J.A., & Pavlu, V. (2007). Query hardness estimation using jensen-shannon divergence among multiple scoring functions. In European conference on information retrieval (pp. 198–209): Springer.

Carmel, D., & Yom-Tov, E. (2010). Estimating the query difficulty for information retrieval. Synthesis Lectures on Information Concepts, Retrieval, and Services, 2(1), 1–89.CrossRef

Carmel, D., Yom-Tov, E., Darlow, A., Pelleg, D. (2006). What makes a query difficult? In Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval (pp. 390–397): ACM.

Chen, C., Yang, M., Li, S., Zhao, T., Qi, H. (2010). Predicting query potential for personalization, classification or regression? In Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval (pp. 725–726): ACM.

Cronen-Townsend, S., Zhou, Y., Croft, W.B. (2002). Predicting query performance. In Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval (pp. 299–306): ACM.

Dan, O., & Davison, B.D. (2016). Measuring and predicting search engine users’ satisfaction. ACM Computing Surveys, 49(1), 18.CrossRef

de Campos, L.M., Fernández-Luna, J.M., Huete, J.F., Romero, A.E. (2006). Garnata: an information retrieval system for structured documents based on probabilistic graphical models. In Proceedings of the eleventh international conference of information processing and management of uncertainty in knowledge-based systems (pp. 1024–1031).

de Campos, L.M., Fernández-Luna, J. M., Huete, J.F., Martín-dancausa, C., Romero, A.E. (2009). New utility models for the garnata information retrieval system at inex’08. In Advances in focused retrieval (pp. 39–45): Springer.

de Campos, L.M., Fernández-Luna, J.M., Huete, J.F., Vicente-López, E. (2014). Using personalization to improve xml retrieval. IEEE Transactions on Knowledge and Data Engineering, 26(5), 1280–1292.CrossRef

Dou, Z., Song, R., Wen, J.R. (2007). A large-scale evaluation and analysis of personalized search strategies. In Proceedings of the 16th international conference on world wide web (pp. 581–590): ACM.

Ghorab, M.R., Zhou, D., O’Connor, A., Wade, V. (2013). Personalised information retrieval: survey and classification. User Modeling and User-Adapted Interaction, 23(4), 381–443.CrossRef

Guo, Q., White, R.W., Dumais, S.T., Wang, J., Anderson, B. (2010). Predicting query performance using query, result, and user interaction features. In Adaptivity, personalization and fusion of heterogeneous information (pp. 198–201): Le Centre de hautes Etudes Internationales d’Informatique Documentaire.

Hauff, C. (2010). Predicting the effectiveness of queries and retrieval systems. Thesis, Centre for Telematics and Information Technology University of Twente.

Hauff, C., Hiemstra, D., de Jong, F. (2008a). A survey of pre-retrieval query performance predictors. In Proceedings of the 17th ACM conference on information and knowledge management (pp. 1419–1420): ACM.

Hauff, C., Murdock, V., Baeza-Yates, R. (2008b). Improved query difficulty prediction for the web. In Proceedings of the 17th ACM conference on information and knowledge management (pp. 439–448): ACM.

Hauff, C., Kelly, D., Azzopardi, L. (2010). A comparison of user and system query performance predictions. In Proceedings of the 19th ACM international conference on information and knowledge management (pp. 979–988): ACM.

He, B., & Ounis, I. (2004). Inferring query performance using pre-retrieval predictors. In International symposium on string processing and information retrieval (pp. 43–54): Springer.

He, J., Larson, M., De Rijke, M. (2008). Using coherence-based measures to predict query difficulty. In European conference on information retrieval (pp. 689–694): Springer.

Järvelin, K., & Kekäläinen, J. (2002). Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems, 20(4), 422–446.CrossRef

Liu, F., Yu, C., Meng, W. (2004). Personalized web search for improving retrieval effectiveness. IEEE transactions on Knowledge and Data Engineering, 16(1), 28–40.CrossRef

Micarelli, A., Gasparetti, F., Sciarrone, F., Gauch, S. (2007). Personalized search on the world wide web. In The adaptive web (pp. 195–230): Springer.

Mothe, J., & Tanguy, L. (2005). Linguistic features to predict query difficulty. In ACM conference on research and development in information retrieval, SIGIR, predicting query difficulty-methods and applications workshop (pp. 7–10).

Shtok, A., Kurland, O., Carmel, D., Raiber, F., Markovits, G. (2012). Predicting query performance by query-drift estimation. ACM Transactions on Information Systems, 30(2), 11.CrossRef

Steichen, B., Ashman, H., Wade, V. (2012). A comparative survey of personalised information retrieval and adaptive hypermedia techniques. Information Processing & Management, 48(4), 698–724.CrossRef

Teevan, J., Dumais, S.T., Liebling, D.J. (2008). 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 (pp. 163–170): ACM.

Teevan, J., Dumais, S.T., Horvitz, E. (2010). Potential for personalization. ACM Transactions on Computer-Human Interaction, 17(1), 4.CrossRef

Vicente-López, E., de Campos, L.M., Fernández-Luna, J.M., Huete, J.F., Tagua-Jiménez, A., Tur-Vigil, C. (2015). An automatic methodology to evaluate personalized information retrieval systems. User Modeling and User-Adapted Interaction, 25(1), 1–37.CrossRef

Vicente-López, E., de Campos, L.M., Fernández-Luna, J.M., Huete, J.F. (2016). Use of textual and conceptual profiles for personalized retrieval of political documents. Knowledge-Based Systems, 112, 127–141.CrossRef

Younus, A., Qureshi, M.A., O’Riordan, C., Pasi, G. (2013). Personalization for difficult queries. SIGIR Workshop on Modeling User Behavior for Information Retrieval Evaluation, 15–16.

Zhang, W., Wang, J., Chen, B., Zhao, X. (2013). To personalize or not: a risk management perspective. In Proceedings of the 7th ACM conference on recommender systems (pp. 229–236): ACM.

Zhao, Y., Scholer, F., Tsegay, Y. (2008). Effective pre-retrieval query performance prediction using similarity and variability evidence. In European conference on information retrieval (pp. 52–64): Springer,.

Zhou, Y., & Croft, W.B. (2006). Ranking robustness: a novel framework to predict query performance. In Proceedings of the 15th ACM international conference on information and knowledge management (pp. 567–574): ACM.

Zhou, Y., & Croft, W.B. (2007). Query performance prediction in web search environments. In Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval (pp. 543–550): ACM.

Zhou, B., & Yao, Y. (2010). Evaluating information retrieval system performance based on user preference. Journal of Intelligent Information Systems, 34(3), 227–248.CrossRef

Titel: Predicting IR personalization performance using pre-retrieval query predictors
verfasst von: Eduardo Vicente-López
Luis M. de Campos
Juan M. Fernández-Luna
Juan F. Huete
Publikationsdatum: 30.01.2018
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems / Ausgabe 3/2018
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-018-0498-3

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