Predicting IR Personalization Performance using Pre-retrieval Query Predictors (2401.13351v1)
Abstract: Personalization generally improves the performance of queries but in a few cases it may also harms it. If we are able to predict and therefore to disable personalization for those situations, the overall performance will be higher and users will be more satisfied with personalized systems. We use some state-of-the-art pre-retrieval query performance predictors and propose some others including the user profile information for the previous purpose. We study the correlations among these predictors and the difference between the personalized and the original queries. We also use classification and regression techniques to improve the results and finally reach a bit more than one third of the maximum ideal performance. We think this is a good starting point within this research line, which certainly needs more effort and improvements.
- In: European conference on information retrieval, pp. 127–137. Springer (2004)
- In: European Conference on Information Retrieval, pp. 198–209. Springer (2007)
- In: Advances in Focused Retrieval, pp. 39–45. Springer (2009)
- In: Proceedings of the Eleventh International Conference of Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 1024–1031 (2006)
- IEEE Transactions on Knowledge and Data Engineering 26(5), 1280–1292 (2014)
- Synthesis Lectures on Information Concepts, Retrieval, and Services 2(1), 1–89 (2010)
- In: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 390–397. ACM (2006)
- In: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pp. 725–726. ACM (2010)
- In: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 299–306. ACM (2002)
- ACM Computing Surveys 49(1), 18 (2016)
- In: Proceedings of the 16th international conference on World Wide Web, pp. 581–590. ACM (2007)
- User Modeling and User-Adapted Interaction 23(4), 381–443 (2013)
- In: Adaptivity, Personalization and Fusion of Heterogeneous Information, pp. 198–201. Le Centre de Hautes Etudes Internationales d’Informatique Documentaire (2010)
- Hauff, C.: Predicting the effectiveness of queries and retrieval systems. Thesis, Centre for Telematics and Information Technology, University of Twente (2010)
- In: Proceedings of the 17th ACM conference on Information and knowledge management, pp. 1419–1420. ACM (2008)
- In: Proceedings of the 19th ACM international conference on Information and knowledge management, pp. 979–988. ACM (2010)
- In: Proceedings of the 17th ACM conference on Information and knowledge management, pp. 439–448. ACM (2008)
- In: International Symposium on String Processing and Information Retrieval, pp. 43–54. Springer (2004)
- In: European Conference on Information Retrieval, pp. 689–694. Springer (2008)
- ACM Transactions on Information Systems 20(4), 422–446 (2002)
- IEEE transactions on Knowledge and Data Engineering 16(1), 28–40 (2004)
- In: The adaptive web, pp. 195–230. Springer (2007)
- In: ACM Conference on research and Development in Information Retrieval, SIGIR, Predicting query difficulty-methods and applications workshop, pp. 7–10 (2005)
- ACM Transactions on Information Systems 30(2), 11 (2012)
- Information Processing & Management 48(4), 698–724 (2012)
- ACM Transactions on Computer-Human Interaction 17(1), 4 (2010)
- In: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pp. 163–170. ACM (2008)
- Knowledge-Based Systems 112, 127–141 (2016)
- User Modeling and User-Adapted Interaction 25(1), 1–37 (2015)
- SIGIR Workshop on Modeling User Behavior for Information Retrieval Evaluation pp. 15–16 (2013)
- In: Proceedings of the 7th ACM conference on Recommender systems, pp. 229–236. ACM (2013)
- In: European Conference on Information Retrieval, pp. 52–64. Springer (2008)
- Journal of Intelligent Information Systems 34(3), 227–248 (2010)
- In: Proceedings of the 15th ACM international conference on Information and knowledge management, pp. 567–574. ACM (2006)
- In: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 543–550. ACM (2007)
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