Designing a Framework for Developing an Adaptive Information Retrieval System that Personalizes Information
Keywords:
Personalized search, user preference, activity information, similar user, ranking, social media.Abstract
As a result of advancements in internet technology, more and more people are turning to the World Wide Web as their primary source of information and education. The academic and business communities have shown considerable interest in personalized search due to its potential to improve the effectiveness of Web searches. In comparison to a standard web search, customized search returns results that are tailored to the individual. Each user of a personalized Internet search will see a unique set of search results tailored to their own set of interests, tastes, and information needs in response to any given query. Unfortunately, the current personalized search methods fall short of fully meeting the needs of the particular user, as they do not take into account either the user's most recent preferences nor the interests of other users. With the rise of Personalized Search, however, comes a new challenge: users' reluctance to reveal sensitive information about themselves during searches. The most common search engines are made with everyone in mind, rather than a specific user in mind; as a result, the results they return for a given query are generic, rather than tailored to the individual user. Numerous algorithms exist to swiftly analyze user preferences and return relevant search results via a personalized web search;. Examples of applications for such algorithms include user tracking, link analysis, textual analysis, and collaborative online search. This paper mainly designs a framework by an adaptive information retrieval system which presents more appropriate information for users. The experimental results show that our proposed framework reduces the search time and improves the efficiency of web search.
Downloads
References
Bin Tan, Xuehua Shen, and ChengXiang Zhai, “Mining Long-Term Search History to Improve Search Accuracy,” In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016.
Lidan Shou, He Bai, Ke Chen, and Gang Chen, “Supporting Privacy Protection in Personalized Web Search”, IEEE transactions on knowledge and data engineering, vol. 26, no. 2, 2017.
Jaime Teevan, Susan T. Dumais, and Eric Horvitz, “Personalizing Search via Automated Analysis of Interests and Activities,” In Proceedings of 28th Ann. International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 449- 456, 2018.
Micro Spertta and Susan Gach, “Personalizing Search Based on User Search Histories,” In Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence (WI), 2018.
Kazunari Sugiyama, Kenji Hatano and Masatoshi Yoshikawa, “Adaptive Web Search Based on User Profile Constructed without any Effort from Users,” In Proceedings of 13th International Conference World Wide Web (WWW), 2018.
Feng Qiu and Junghoo Cho, “Automatic Identification of User Interest for Personalized Search,” In Proceedings of 15th International Conference World Wide Web(WWW), pp. 727-736, 2016.
Zhicheng Dou, Ruihua Song, and Ji-Rong Wen, “A Large-Scale Evaluation and Analysis of Personalized Search Strategies,” In Proceedings of International Conference World Wide Wed (WWW), pp. 581-590, 2017.
J. Pitkow, H. Schutze, T. Cass, R. Cooley, D. Turnbull, A. Edmonds, E. Adar, and T. Breuel, “Personalized Search,” Comm. ACM, vol. 45, no. 9, pp. 50-55, 2012.
Yabo Xu, Benyu Zhang, Zheng Chen, Ke Wang, “Privacy-Enhancing Personalized Web Search,” In Proceedings of 16th International Conference World Wide Web (WWW), pp. 591-600, 2017.
Qin, T., et al.: LETOR: A benchmark collection for research on learning to rank for information retrieval. Information Retrieval 13, 346–374 (2010)
Liu, Y., et al.: How do users describe their information need: Query recommendation based on snippet click model. Expert Systems with Applications 38, 13847–13856 (2011)
Nguyen, D.Q., et al.: A capsule network-based embedding model for knowledge graph completion and search personalization. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2180–2189 (2019)
Yao, J., Dou, Z., Wen, J.: Employing personal word embeddings for personalized search. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1359–1368 (2020)
Lu, S., et al.: Knowledge enhanced personalized search. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 709–718 (2020)
Paul Alexandru Chirita, Wolfgang Nejdl, Raluca Paiu, and Christian Kohlschutter, “Using ODP Metadata to Personalize Search,” In Proceedings of 28th Ann. International ACM SIGIR Conference on Research and Development Information Retrieval (SIGIR), 2015.
Krishnan Ramanathan, Julien Giraudi, and Ajay Gupta, “Creating Hierarchical User Profiles Using Wikipedia,” HP Labs, 2008.
Horowitz, D.; Kamvar, S.D. The anatomy of a large-scale social search engine. In Proceedings of the International Conference on World Wide Web, Raleigh, NC, USA, 26-30 April 2017.
Bao, S.; Xue, G.; Wu, X.; Yu, Y.; Fei, B.; Su, Z. Optimizing web search using social annotations. In Proceedings of the International Conference on World Wide Web, Banff, AB, Canada, 8–12 May 2017.
Kashyap, A.; Amini, R.; Hristidis, V. SonetRank: Leveraging social networks to personalize search. In Proceedings of the International Conference on Information and Knowledge Management, Maui, HI, USA, 29 October–2 November 2012.
Shafiq, M.O.; Alhajj, R.; Rokne, J.G. On personalizing Web search using social network analysis. Inf. Sci. 2015, 314, 55–76.
Abu-Salih, B.; Alsawalqah, H.; Elshqeirat, B.; Issa, T.; Wongthongtham, P.; Premi, K. Toward a Knowledge-based Personalised Recommender System for Mobile App Development. J. Univers. Comput. Sci. 2021, 27, 208–229.
Kim, Y.A.; Park, G.W. Topic-Driven SocialRank: Personalized search result ranking by identifying similar, credible users in a social network. Knowl. Based Syst. 2013, 54, 230–242.
Bok, K.; Lim, J.; Ahn, M.; Yoo, J. A Social Search Scheme Considering User Preferences and Popularities in Mobile Environments.KSII Trans. Internet Inf. Syst. 2016, 10, 744–768.
Li, J.; Liu, L.; Zhang, S.; Ma, S.; Le, T.D.; Liu, J. Causal heterogeneity discovery by bottom-up pattern search for personalised decision making. Appl. Intell. 2022, 1–15.
Esposito, M.; Picchiami, L. A Comparative Study of AI Search Methods for Personalised Cancer Therapy Synthesis in COPASI. In Proceedings of the International Conference of the Italian Association for Artificial Intelligence, Milan, Italy, 1–3 December 2021.
Han, W.; Tian, Z.; Zhu, C.; Huang, Z.; Jia, Y.; Guizani, M. A Topic Representation Model for Online Social Networks Based on Hybrid Human-Artificial Intelligence. IEEE Trans. Comput. Soc. Syst. 2021, 8, 191–200.
Abu-Salih, B.; Wongthongtham, P.; Chan, K.Y. Twitter mining for ontology-based domain discovery incorporating machine learning. J. Knowl. Manag. 2018, 22, 949–981.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
