short-paper

Lightweight Unbiased Multi-teacher Ensemble for Review-based Recommendation

Authors:

Zhenhua HuangAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 4620 - 4624

https://doi.org/10.1145/3511808.3557629

Published: 17 October 2022 Publication History

Abstract

Review-based recommender systems (RRS) have received an increasing interest since reviews greatly enhance recommendation quality and interpretability. However, existing RRS suffer from high computational complexity, biased recommendation and poor generalization. The three problems make them inadequate to handle real recommendation scenarios. Previous studies address each issue separately, while none of them consider solving three problems together under a unified framework. This paper presents LUME (a Lightweight Unbiased Multi-teacher Ensemble) for RRS. LUME is a novel framework that addresses the three problems simultaneously. LUME uses multi-teacher ensemble and debiased knowledge distillation to aggregate knowledge from multiple pretrained RRS, and generates a small, unbiased student recommender which generalizes better. Extensive experiments on various real-world benchmarks demonstrate that LUME successfully tackles the three problems and has superior performance than state-of-the-art RRS and knowledge distillation based RS.

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Cited By

Yang WHuo TLiu ZLu CChen HDuh WHuang HKato MMothe JPoblete B(2023)Review-based Multi-intention Contrastive Learning for RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592053(2339-2343)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592053
Wang XYue HWang ZXu LZhang J(2023)Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential Recommendation2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00194(1526-1534)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00194

Index Terms

Lightweight Unbiased Multi-teacher Ensemble for Review-based Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

Copyright © 2022 ACM.

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Published: 17 October 2022

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Natural Science Foundation of China
Alibaba Innovative Research program

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CIKM '22

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CIKM '22: The 31st ACM International Conference on Information and Knowledge Management

October 17 - 21, 2022

GA, Atlanta, USA

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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Yang WHuo TLiu ZLu CChen HDuh WHuang HKato MMothe JPoblete B(2023)Review-based Multi-intention Contrastive Learning for RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592053(2339-2343)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592053
Wang XYue HWang ZXu LZhang J(2023)Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential Recommendation2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00194(1526-1534)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00194

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