Abstract
To improve personalized search, we need to increase the efficiency of personalization models using effective user profiles and ranking models. The ranking models improve accuracy by combining personalized and non-personalized models. In the personalized models, user profiles are used to re-rank the results, while in non-personalized models documents are ranked in the absence of user profile. A personalization metric able to estimate the potential for personalization can enable the selective application of personalization and improve the overall effectiveness of the search system. In this paper, a personalization fuzzy topic model (FTM) is proposed for integrating the topical user profile into the personalized web search. The topical user profile is built using the fuzzy logic in handling the uncertainty of the occurrence of all topics in a document, and the fuzzy c-means algorithm is used to retrieve the relevant topics. To evaluate the proposed model, the ranking results using the proposed Personalized-FTM are compared against personalization using the Latent Dirichlet Allocation model. The result reveals that the Personalized-FTM improves the Mean Reciprocal Rank and the Normalized Discounted Cumulative Gain by 7% and 5%, respectively, for all topic numbers.
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Gensim library is used for the LDA estimation https://radimrehurek.com/gensim/.
References
Harvey, M.; Crestani, F.; Carman, M.: Building user profiles from topic models for personalised search. In: Proceedings of the 22nd ACM International Conference on Conference on Information and Knowledge Management, pp. 2309–2314 (2013)
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, pp. 1849–1852 (2010)
Porteous, I.; Newman, D.; Ihler, A.; Asuncion, A.; Smyth, P.; Welling, M.: Fast collapsed Gibbs sampling for latent Dirichlet allocation. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2008)
Wang, X.; McCallum, A.: Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2006)
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 (PIA). pp. 84–92 (2005)
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, pp. 581–590 (2007)
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, pp. 1877–1880 (2016)
Teevan, J.; Dumais, S.; Horvitz, E.: Potential for personalization. In: ACM Transactions on Computer–Human Interaction (TOCHI) TOCHI, pp. 1–31 (2010)
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, pp. 163–170 (2008)
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, pp. 299–306 (2002)
Sara, A., Rayan, A., Salih, C.: A classification on different aspects of user modelling in personalized web search. In: 4th International Conference on Natural Language Processing and Information Retrieval (2020)
Wang, Y.; Agichtein, E.: Query ambiguity revisited: clickthrough measures for distinguishing informational and ambiguous queries. In: HLT ’10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 361–364 (2010)
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, pp. 1169–1170 (2007)
Sara, A.; Rayan, A.; Salih, Ç.: Personalized web search using key phrase-based user profiles. In: 19th International Conference on WWW/Internet (ICWI) (2020)
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, pp. 131–132 (2015)
Vu, T.; Willis, A.; Tran, S.; Song, D.: Temporal latent topic user profiles for search personalisation. In: ECIR: 37th European Conference on IR Research, pp. 605–616 (2015)
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, pp. 289–296 (1999)
Sara, A.; Rayan, A.; Salih, Ç.: Group-based personalization using topical user profile. In: UMAP ’20 Adjunct: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, pp. 181–186 (2020)
Sara, A.; Rayan, A.; Salih, Ç.: Estimating personalization using topical user profile. In: 12th International Conference on Knowledge Discovery and Information Retrieval (2020)
Vu, T.; Willis, A.; Kruschwitz, U.; Song, D.: Personalised query suggestion for intranet search with temporal user profiling. In: Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval, pp. 265–268 (2017)
Bennett, P., White, R., Chu, W., Dumais, S., Bailey, P., Borisyuk, F., Cui, X.: 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, pp. 185–194 (2012)
Karimi, S.; Abri, R.: Improvement of semantic search results with providing an updatable dynamic user model. Int. J. Comput. Appl. 155(4), 7–14 (2016). https://doi.org/10.5120/ijca2016912285
Wallach, H. M.: Topic modeling: beyond bag-of-words. In: Proceedings of the 23rd International Conference on Machine Learning (2006)
Mimno, D.; Wallach, H.M.; Talley, E.; Leenders, M.; McCallum, A.: Optimizing semantic coherence in topic models. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2011)
Newman, D.; Bonilla, E.V.; Buntine, W.: Improving topic coherence with regularized topic models. In: Paper Presented at the Advances in Neural Information Processing Systems (2011)
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 (SEKEIE 2014): Posters (2014)
Momtazi, S.; Lindenberg, F.: Generating query suggestions by exploiting latent semantics in query logs. J. Inf. Sci. 42, 437–448 (2016)
Jursic, M., Lavrac, N.: Fuzzy clustering of documents. In: Paper Presented at the Conference on Data Mining and Data Warehouses (SiKDD 2008) (2008)
Patil, D.B.; Dongre, Y.V.: A fuzzy approach for text mining. IJ Math. Sci. Comput. 4, 34–43 (2015)
Rodrigues, M.M.; Sacks, L.: A scalable hierarchical fuzzy clustering algorithm for text mining. In: Paper Presented at the Proceedings of the 5th International Conference on Recent Advances in Soft Computing (2004)
Kraft, D.H.; Bordogna, G.; Pasi, G.: Fuzzy Set Techniques in Information Retrieval Fuzzy Sets in Approximate Reasoning and Information Systems, vol. 469-510. Springer, Berlin (1999)
Ayeldeen, H., Hassanien, A. E., Fahmy, A. A.: Fuzzy clustering and categorization of text documents. In: 2013 13th International Conference on Paper Presented at the Hybrid Intelligent Systems (HIS) (2013)
Trappey, A.J.; Trappey, C.V.; Hsu, F.C.; Hsiao, D.W.: A fuzzy ontological knowledge document clustering methodology. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 39(3), 806–814 (2009)
Abualigah, L.M.Q..: Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering (2019)
Kummamuru, K.; Dhawale, A.; Krishnapuram, R.: Fuzzy co-clustering of documents and keywords. In: The 12th IEEE International Conference on Paper Presented at the Fuzzy Systems. FUZZ’03 (2003)
Karami, A.; Gangopadhyay, A.; Zhou, B.; Karrazi, H.: Flatm: a fuzzy logic approach topic model for medical documents. In: Paper Presented at the 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) Held Jointly with 2015 5th World Conference on Soft Computing (WConSC) (2015)
Prihatini, P.M.; Putra, I.; Giriantari, I.D.; Sudarma, M.: Fuzzy-gibbs latent Dirichlet allocation model for feature extraction on Indonesian documents. Contemp. Eng. Sci. 10, 403–421 (2017)
Rashid, J.; Shah, S.M.A..; Irtaza, A.: Fuzzy topic modeling approach for text mining over short text. Information Processing and Management (2019)
Bezdek, J.C.; Ehrlich, R.; Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)
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Abri, S., Abri, R. Providing a Personalization Model Based on Fuzzy Topic Modeling. Arab J Sci Eng 46, 3079–3086 (2021). https://doi.org/10.1007/s13369-020-05048-7
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DOI: https://doi.org/10.1007/s13369-020-05048-7