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Multi-task learning for learning to rank in web search

Published: 02 November 2009 Publication History

Abstract

Both the quality and quantity of training data have significant impact on the performance of ranking functions in the context of learning to rank for web search. Due to resource constraints, training data for smaller search engine markets are scarce and we need to leverage existing training data from large markets to enhance the learning of ranking function for smaller markets. In this paper, we present a boosting framework for learning to rank in the multi-task learning context for this purpose. In particular, we propose to learn non-parametric common structures adaptively from multiple tasks in a stage-wise way. An algorithm is developed to iteratively discover super-features that are effective for all the tasks. The estimation of the functions for each task is then learned as a linear combination of those super-features. We evaluate the performance of this multi-task learning method for web search ranking using data from a search engine. Our results demonstrate that multi-task learning methods bring significant relevance improvements over existing baseline methods.

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Cited By

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  • (2023)Additive multi-task learning models and task diagnosticsCommunications in Statistics - Simulation and Computation10.1080/03610918.2023.221243053:12(6120-6137)Online publication date: 19-May-2023
  • (2022)Mixed-integer quadratic programming reformulations of multi-task learning modelsMathematics in Engineering10.3934/mine.20230205:1(1-16)Online publication date: 2022
  • (2022)Multi-Aspect Dense RetrievalProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539137(3178-3186)Online publication date: 14-Aug-2022
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    cover image ACM Conferences
    CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management
    November 2009
    2162 pages
    ISBN:9781605585123
    DOI:10.1145/1645953
    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: 02 November 2009

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    Author Tags

    1. experimental evaluation
    2. learning to rank
    3. multi-task learning
    4. relevance

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    View all
    • (2023)Additive multi-task learning models and task diagnosticsCommunications in Statistics - Simulation and Computation10.1080/03610918.2023.221243053:12(6120-6137)Online publication date: 19-May-2023
    • (2022)Mixed-integer quadratic programming reformulations of multi-task learning modelsMathematics in Engineering10.3934/mine.20230205:1(1-16)Online publication date: 2022
    • (2022)Multi-Aspect Dense RetrievalProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539137(3178-3186)Online publication date: 14-Aug-2022
    • (2022)A Survey on Multi-Task LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.307020334:12(5586-5609)Online publication date: 1-Dec-2022
    • (2022)Architectural patterns for the design of federated learning systemsJournal of Systems and Software10.1016/j.jss.2022.111357191:COnline publication date: 1-Sep-2022
    • (2022)Design Perspectives of Multi‐task Deep‐Learning Models and ApplicationsMachine Learning Algorithms for Signal and Image Processing10.1002/9781119861850.ch4(45-63)Online publication date: 18-Nov-2022
    • (2021)A Review of Graph-Based Models for Entity-Oriented SearchSN Computer Science10.1007/s42979-021-00828-w2:6Online publication date: 30-Aug-2021
    • (2020)Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label SpaceProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412713(2605-2612)Online publication date: 19-Oct-2020
    • (2020)Selecting Auxiliary Data Using Knowledge Graphs for Image Classification with Limited Labels2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW50498.2020.00473(4026-4031)Online publication date: Jun-2020
    • (2020)Real-Time Detection of Fake-Shops through Machine Learning2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378204(2254-2263)Online publication date: 10-Dec-2020
    • Show More Cited By

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