research-article

KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems

Authors:

Tat-Seng ChuaAuthors Info & Claims

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

Pages 540 - 550

https://doi.org/10.1145/3511808.3557220

Published: 17 October 2022 Publication History

Abstract

The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive. This issue is usually approached by utilizing the interaction history to conduct offline evaluation. However, existing datasets of user-item interactions are partially observed, leaving it unclear how and to what extent the missing interactions will influence the evaluation. To answer this question, we collect a fully-observed dataset from Kuaishou's online environment, where almost all 1,411 users have been exposed to all 3,327 items. To the best of our knowledge, this is the first real-world fully-observed data with millions of user-item interactions.

With this unique dataset, we conduct a preliminary analysis of how the two factors - data density and exposure bias - affect the evaluation results of multi-round conversational recommendation. Our main discoveries are that the performance ranking of different methods varies with the two factors, and this effect can only be alleviated in certain cases by estimating missing interactions for user simulation. This demonstrates the necessity of the fully-observed dataset. We release the dataset and the pipeline implementation for evaluation at https://kuairec.com

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Cited By

Lin KMansoury MEskandanian FSabouri MMobasher BBoratto LGena CMarras MGermanakos PPopescus E(2024)Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated RecommendationAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664869(86-91)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664869
Cavenaghi EZanga AStella FZanker M(2024)Towards a Causal Decision-Making Framework for Recommender SystemsACM Transactions on Recommender Systems10.1145/36291692:2(1-34)Online publication date: 14-May-2024
https://dl.acm.org/doi/10.1145/3629169
Echterhoff JMelkote AKancherla SMcAuley J(2024)Avoiding Decision Fatigue with AI-Assisted Decision-MakingProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659569(1-11)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659569
Show More Cited By

Index Terms

KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
    2. 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

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Cited By

Lin KMansoury MEskandanian FSabouri MMobasher BBoratto LGena CMarras MGermanakos PPopescus E(2024)Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated RecommendationAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664869(86-91)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664869
Cavenaghi EZanga AStella FZanker M(2024)Towards a Causal Decision-Making Framework for Recommender SystemsACM Transactions on Recommender Systems10.1145/36291692:2(1-34)Online publication date: 14-May-2024
https://dl.acm.org/doi/10.1145/3629169
Echterhoff JMelkote AKancherla SMcAuley J(2024)Avoiding Decision Fatigue with AI-Assisted Decision-MakingProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659569(1-11)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659569
Yu YGao CChen JTang HSun YChen QMa WZhang MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender SystemsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657868(977-987)Online publication date: 10-Jul-2024
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Xu XDong HQi LZhang XXiang HXia XXu YDou WHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)CMCLRec: Cross-modal Contrastive Learning for User Cold-start Sequential RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657839(1589-1598)Online publication date: 10-Jul-2024
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Huang JOosterhuis HMansoury Mvan Hoof Hde Rijke MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender SystemsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657749(416-426)Online publication date: 10-Jul-2024
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Chen SChen JZhou SWang BHan SSu CYuan YWang CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)SIGformer: Sign-aware Graph Transformer for RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657747(1274-1284)Online publication date: 10-Jul-2024
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Chen JWenjie WGao CWu PWei JHua QHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Treatment Effect Estimation for User Interest Exploration on Recommender SystemsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657736(1861-1871)Online publication date: 10-Jul-2024
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Niu YXing XJia ZLiu RXin MCui JChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Diffusion Recommendation with Implicit Sequence InfluenceCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651951(1719-1725)Online publication date: 13-May-2024
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