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Personalized ranking model adaptation for web search

Published: 28 July 2013 Publication History

Abstract

Search engines train and apply a single ranking model across all users, but searchers' information needs are diverse and cover a broad range of topics. Hence, a single user-independent ranking model is insufficient to satisfy different users' result preferences. Conventional personalization methods learn separate models of user interests and use those to re-rank the results from the generic model. Those methods require significant user history information to learn user preferences, have low coverage in the case of memory-based methods that learn direct associations between query-URL pairs, and have limited opportunity to markedly affect the ranking given that they only re-order top-ranked items.
In this paper, we propose a general ranking model adaptation framework for personalized search. Using a given user-independent ranking model trained offline and limited number of adaptation queries from individual users, the framework quickly learns to apply a series of linear transformations, e.g., scaling and shifting, over the parameters of the given global ranking model such that the adapted model can better fit each individual user's search preferences. Extensive experimentation based on a large set of search logs from a major commercial Web search engine confirms the effectiveness of the proposed method compared to several state-of-the-art ranking model adaptation methods.

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Cited By

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  • (2024)Encoding Group Interests With Persistent Homology for Personalized SearchIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2024.341002954:9(5606-5616)Online publication date: Sep-2024
  • (2024)Compassion with Mission: Personalization of Task Ranking Using ChatGPT2024 IEEE 5th International Conference on Pattern Recognition and Machine Learning (PRML)10.1109/PRML62565.2024.10779938(138-142)Online publication date: 19-Jul-2024
  • (2024)How to personalize and whether to personalize? Candidate documents decideKnowledge and Information Systems10.1007/s10115-024-02138-y66:9(5581-5604)Online publication date: 27-May-2024
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    cover image ACM Conferences
    SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
    July 2013
    1188 pages
    ISBN:9781450320344
    DOI:10.1145/2484028
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 28 July 2013

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    Author Tags

    1. learning to rank
    2. model adaptation
    3. personalization

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    SIGIR '13 Paper Acceptance Rate 73 of 366 submissions, 20%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    Cited By

    View all
    • (2024)Encoding Group Interests With Persistent Homology for Personalized SearchIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2024.341002954:9(5606-5616)Online publication date: Sep-2024
    • (2024)Compassion with Mission: Personalization of Task Ranking Using ChatGPT2024 IEEE 5th International Conference on Pattern Recognition and Machine Learning (PRML)10.1109/PRML62565.2024.10779938(138-142)Online publication date: 19-Jul-2024
    • (2024)How to personalize and whether to personalize? Candidate documents decideKnowledge and Information Systems10.1007/s10115-024-02138-y66:9(5581-5604)Online publication date: 27-May-2024
    • (2023)Personalized and Diversified: Ranking Search Results in an Integrated WayACM Transactions on Information Systems10.1145/363198942:3(1-25)Online publication date: 9-Nov-2023
    • (2023)Adversarially Trained Environment Models Are Effective Policy Evaluators and Improvers - An Application to Information RetrievalProceedings of the Fifth International Conference on Distributed Artificial Intelligence10.1145/3627676.3627680(1-12)Online publication date: 30-Nov-2023
    • (2023)Incorporating Explicit Subtopics in Personalized SearchProceedings of the ACM Web Conference 202310.1145/3543507.3583488(3364-3374)Online publication date: 30-Apr-2023
    • (2023)Personalized Secure Demand-Oriented Data Service Toward Edge-Cloud Collaborative IoTIEEE Internet of Things Journal10.1109/JIOT.2022.319993710:1(378-390)Online publication date: 1-Jan-2023
    • (2023)Personalized Semantic Matching for Web Search2023 IEEE 39th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW58674.2023.00037(205-210)Online publication date: Apr-2023
    • (2022)Providing A Topic-Based LSTM Model to Re-Rank Search ResultsProceedings of the 2022 7th International Conference on Machine Learning Technologies10.1145/3529399.3529438(249-254)Online publication date: 11-Mar-2022
    • (2022)Improving Personalized Search with Dual-Feedback NetworkProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498447(210-218)Online publication date: 11-Feb-2022
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