On Challenges of Evaluating Recommender Systems in an Offline Setting

Author:

Sun Aixin1ORCID

Affiliation:

1. Nanyang Technological University, Singapore

Publisher

ACM

Reference21 articles.

1. Bushra Alhijawi , Arafat Awajan , and Salam Fraihat . 2022. Survey on the Objectives of Recommender Systems: Measures , Solutions, Evaluation Methodology, and New Perspectives. ACM Comput. Surv. 55, 5, Article 93 ( 2022 ). https://doi.org/10.1145/3527449 10.1145/3527449 Bushra Alhijawi, Arafat Awajan, and Salam Fraihat. 2022. Survey on the Objectives of Recommender Systems: Measures, Solutions, Evaluation Methodology, and New Perspectives. ACM Comput. Surv. 55, 5, Article 93 (2022). https://doi.org/10.1145/3527449

2. Offline recommender system evaluation: Challenges and new directions

3. Common Pitfalls in Training and Evaluating Recommender Systems

4. Paolo Cremonesi and Dietmar Jannach . 2021. Progress in Recommender Systems Research: Crisis? What Crisis?AI Magazine 42, 3 (Nov . 2021 ), 43–54. https://doi.org/10.1609/aimag.v42i3.18145 10.1609/aimag.v42i3.18145 Paolo Cremonesi and Dietmar Jannach. 2021. Progress in Recommender Systems Research: Crisis? What Crisis?AI Magazine 42, 3 (Nov. 2021), 43–54. https://doi.org/10.1609/aimag.v42i3.18145

5. Maurizio Ferrari Dacrema Paolo Cremonesi and Dietmar Jannach. 2019. Are we really making much progress? A worrying analysis of recent neural recommendation approaches. In RecSys. ACM 101–109. Maurizio Ferrari Dacrema Paolo Cremonesi and Dietmar Jannach. 2019. Are we really making much progress? A worrying analysis of recent neural recommendation approaches. In RecSys. ACM 101–109.

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