short-paper

IPR: Interaction-level Preference Ranking for Explicit feedback

Authors:

Hsien Hao Chen,

Chih-Ming Chen,

Ming-Feng Tsai,

Chuan-Ju WangAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1912 - 1916

https://doi.org/10.1145/3477495.3531777

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

Wang Z(2024)Empowering Few-Shot Recommender Systems With Large Language Models-Enhanced RepresentationsIEEE Access10.1109/ACCESS.2024.336802712(29144-29153)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3368027
Wei SWang ZAn XLi QXiao HXiao Y(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
https://dl.acm.org/doi/10.1016/j.knosys.2023.111359
Quan STan HLiu SZheng ZZhu RLi LLu QWu FFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(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
https://dl.acm.org/doi/10.1145/3583780.3614964
Show More Cited By

Index Terms

IPR: Interaction-level Preference Ranking for Explicit feedback
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Learning to rank

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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

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

Copyright © 2022 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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New York, NY, United States

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Published: 07 July 2022

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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Cited By

Wang Z(2024)Empowering Few-Shot Recommender Systems With Large Language Models-Enhanced RepresentationsIEEE Access10.1109/ACCESS.2024.336802712(29144-29153)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3368027
Wei SWang ZAn XLi QXiao HXiao Y(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
https://dl.acm.org/doi/10.1016/j.knosys.2023.111359
Quan STan HLiu SZheng ZZhu RLi LLu QWu FFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(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
https://dl.acm.org/doi/10.1145/3583780.3614964
Lee SLee ESeo Y(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
https://doi.org/10.1007/s12652-023-04714-6

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