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IPR: Interaction-level Preference Ranking for Explicit feedback

Published: 07 July 2022 Publication History

Abstract

Explicit feedback---user input regarding their interest in an item---is the most helpful information for recommendation as it comes directly from the user and shows their direct interest in the item. Most approaches either treat the recommendation given such feedback as a typical regression problem or regard such data as implicit and then directly adopt approaches for implicit feedback; both methods, however,tend to yield unsatisfactory performance in top-k recommendation. In this paper, we propose interaction-level preference ranking(IPR), a novel pairwise ranking embedding learning approach to better utilize explicit feedback for recommendation. Experiments conducted on three real-world datasets show that IPR yields the best results compared to six strong baselines.

Supplementary Material

MP4 File (SIGIR22-sp1326.mp4)
This video introduces the paper: IPR: Interaction-level Preference Ranking for Explicit Feedback. Firstly, we explain the reason why we want to propose IPR in the motivation section. Secondly, we introduce some related works and their limitations which inspire us to design our model. Then, we detail the proposed IPR, which incorporates useful information distilled from all explicit feedback in a less assumptive, natural, and weight-free manner. Last but not least, the conclusions briefly summarize our contributions.

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Cited By

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  • (2024)Empowering Few-Shot Recommender Systems With Large Language Models-Enhanced RepresentationsIEEE Access10.1109/ACCESS.2024.336802712(29144-29153)Online publication date: 2024
  • (2024)A recommendation model for e-commerce platforms oriented to explicit information compensation and hidden information miningKnowledge-Based Systems10.1016/j.knosys.2023.111359286:COnline publication date: 17-Apr-2024
  • (2023)MERIT: A Merchant Incentive Ranking Model for Hotel Search & RankingProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614964(2085-2094)Online publication date: 21-Oct-2023
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  1. IPR: Interaction-level Preference Ranking for Explicit feedback

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    cover image ACM Conferences
    SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2022
    3569 pages
    ISBN:9781450387323
    DOI:10.1145/3477495
    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: 07 July 2022

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    Author Tags

    1. collaborative filtering
    2. explicit feedback
    3. high-order graph information
    4. matrix factorization
    5. top-k recommendation

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    Cited By

    View all
    • (2024)Empowering Few-Shot Recommender Systems With Large Language Models-Enhanced RepresentationsIEEE Access10.1109/ACCESS.2024.336802712(29144-29153)Online publication date: 2024
    • (2024)A recommendation model for e-commerce platforms oriented to explicit information compensation and hidden information miningKnowledge-Based Systems10.1016/j.knosys.2023.111359286:COnline publication date: 17-Apr-2024
    • (2023)MERIT: A Merchant Incentive Ranking Model for Hotel Search & RankingProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614964(2085-2094)Online publication date: 21-Oct-2023
    • (2023)An integration method for optimizing the use of explicit and implicit feedback in recommender systemsJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-023-04714-614:12(16995-17008)Online publication date: 13-Oct-2023

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