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Recommender models are hard to evaluate, particularly under offline setting.
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Revisiting offline evaluation for implicit-feedback recommender systems. In RecSys , Toine Bogers, Alan Said, Peter Brusilovsky, and Domonkos Tikk (Eds.). ACM, 596–600
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Statistically robust evaluation of stream-based recommender systems
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Explainable Reasoning over Knowledge Graphs for Recommendation. In AAAI . AAAI Press, 5329–5336
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Offline evaluation options for recommender systems
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Time Matters: Sequential Recommendation with Complex Temporal Information. In SIGIR . ACM, 1459–1468
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Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, and Tommaso Di Noia. 2021 · 2021
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Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems. In RecSys . ACM, 411–421
Danni Peng, Sinno Jialin Pan, Jie Zhang, and Anxiang Zeng. 2021 · 2021
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Time-dependent Evaluation of Recommender Systems. In RecSys (CEUR Workshop Proceedings, Vol. 2955) . CEUR-WS.org
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Sequence or Pseudo-Sequence? An Analysis of Sequential Recommendation Datasets. In Proceedings of the Perspectives on the Evaluation of Recommender Systems Workshop 2021 co-located with RecSys 2021 (CEUR Workshop Proceedings, Vol. 2955) . CEUR-WS.org
Daniel Woolridge, Sean Wilner, and Madeleine Glick. 2021 · 2021
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Causal Intervention for Leveraging Popularity Bias in Recommendation. In SIGIR . ACM, 11–20
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RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms. In CIKM . ACM, 4653–4664
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The Datasets Dilemma: How Much Do We Really Know About Recommendation Datasets?. In WSDM . ACM, 141–149
Jin Yao Chin, Yile Chen, and Gao Cong. 2022 · 2022
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Do Loyal Users Enjoy Better Recommendations? Understanding Recommender Accuracy from a Time Perspective. In ICTIR
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CVTT: Cross-Validation Through Time
Sergey Kolesnikov and Mikhail Andronov. 2022 · 2022
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Evaluating Recommender Systems: Survey and Framework
Eva Zangerle and Christine Bauer. 2022 · 2022
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A Revisiting Study of Appropriate Offline Evaluation for Top-N Recommendation Algorithms
Wayne Xin Zhao, Zihan Lin, Zhichao Feng, Pengfei Wang, and Ji-Rong Wen. 2022 · 2022
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