Reproducibility in Recommender Systems: A Survey

• URL: http://arxiv.org/abs/2607.26074v1
• Date: Mon, 13 Jul 2026 16:30:47 GMT
• Title: Reproducibility in Recommender Systems: A Survey
• Abstract summary: The ACM RecSys conference introduced a dedicated Reproducibility Track in 2020 to encourage rigorous, transparent, and repeatable research.<n>This paper presents a structured analysis of the track from 2020 to 2025 covering 51 accepted papers.<n>We classify contributions by type and analyze common patterns in datasets, algorithms, frameworks, and evaluation practices.
• Score: 45.98861215799349
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Reproducibility has become a cornerstone of credible recommender systems research, driven by growing concerns about the reliability and generalizability of experimental results. In response, the ACM RecSys conference introduced a dedicated Reproducibility Track in 2020 to encourage rigorous, transparent, and repeatable research. This paper presents a structured analysis of the track from 2020 to 2025, covering 51 accepted papers. We classify contributions by type and analyze common patterns in datasets, algorithms, frameworks, and evaluation practices, with the goal of understanding how reproducibility is operationalized in practice within the community. Our findings reveal three main trends. First, the track has expanded in scope, evolving from a focus on reproduction and replication to include benchmarking, resources, and methodological contributions. Second, reproducibility papers exhibit a consistent methodological profile, relying on a limited set of datasets, algorithms, and evaluation protocols. Third, reproducibility in practice often involves extending prior experiments rather than strictly replicating them, with studies frequently introducing additional models or evaluation criteria. Overall, reproducibility work has improved transparency and documentation, but has had limited impact on methodological diversity, highlighting a gap between the conceptual definition of reproducibility and its implementation.

Related papers

• The Story is Not the Science: Execution-Grounded Evaluation of Mechanistic Interpretability Research [56.80927148740585]
  We address the challenges of scalability and rigor by flipping the dynamic and developing AI agents as research evaluators.<n>We use mechanistic interpretability research as a testbed, build standardized research output, and develop MechEvalAgent.<n>Our work demonstrates the potential of AI agents to transform research evaluation and pave the way for rigorous scientific practices.
  arXiv  Detail & Related papers  (2026-02-05T19:00:02Z)
• Assessing Reproducibility in Evolutionary Computation: A Case Study using Human- and LLM-based Assessment [2.0365636651755263]
  We study the practices in papers published in the Combinatorial Optimization and Metaheuristics track of the Evolutionary Computation Conference over a ten-year period.<n>We introduce a structured checklist and apply it through a systematic manual assessment of the selected corpus.<n>In addition, we propose RECAP (REproducibility Checklist Automation Pipeline), an automated system that automatically evaluates signals from paper text and associated code.
  arXiv  Detail & Related papers  (2026-02-05T08:32:29Z)
• Character-R1: Enhancing Role-Aware Reasoning in Role-Playing Agents via RLVR [67.66592867046229]
  Character-R1 is a framework designed to provide verifiable reward signals for effective role-aware reasoning.<n>Our framework comprises three core designs: Cognitive Focus Reward, Reference-Guided Reward and Character-Conditioned Reward Normalization.
  arXiv  Detail & Related papers  (2026-01-08T05:33:37Z)
• Large Language Models for Software Engineering: A Reproducibility Crisis [4.730658148470817]
  This paper presents the first large-scale, empirical study of practices in large language model (LLM)-based software engineering research.<n>We systematically mined and analyzed 640 papers published between 2017 and 2025 across premier software engineering, machine learning, and natural language processing venues.<n>Our analysis reveals persistent gaps in artifact availability, environment specification, versioning rigor, and documentation clarity.
  arXiv  Detail & Related papers  (2025-11-29T22:16:47Z)
• Continuous Representation Methods, Theories, and Applications: An Overview and Perspectives [55.22101595974193]
  Recently, continuous representation methods emerge as novel paradigms that characterize the intrinsic structures of real-world data.<n>This review focuses on three aspects: (i) Continuous representation method designs such as basis function representation, statistical modeling, tensor function decomposition, and implicit neural representation; (ii) Theoretical foundations of continuous representations such as approximation error analysis, convergence property, and implicit regularization; and (iii) Real-world applications of continuous representations derived from computer vision, graphics, bioinformatics, and remote sensing.
  arXiv  Detail & Related papers  (2025-05-21T07:50:19Z)
• Evaluating Rank-N-Contrast: Continuous and Robust Representations for Regression [44.99833362998488]
  This document is an evaluation of the original "Rank-N-Contrast" (arXiv:2210.01189v2) paper published in 2023.
  arXiv  Detail & Related papers  (2024-11-25T11:31:53Z)
• A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [51.26815896167173]
  We present a comprehensive tertiary analysis of PAMI reviews along three complementary dimensions.<n>Our analyses reveal distinctive organizational patterns as well as persistent gaps in current review practices.<n>Finally, our evaluation of state-of-the-art AI-generated reviews indicates encouraging advances in coherence and organization.
  arXiv  Detail & Related papers  (2024-02-20T11:28:50Z)
• Standardizing Your Training Process for Human Activity Recognition Models: A Comprehensive Review in the Tunable Factors [4.199844472131922]
  We provide an exhaustive review of contemporary deep learning research in the field of wearable human activity recognition (WHAR) Our findings suggest that a major trend is the lack of detail provided by model training protocols. With insights from the analyses, we define a novel integrated training procedure tailored to the WHAR model.
  arXiv  Detail & Related papers  (2024-01-10T17:45:28Z)
• Benchopt: Reproducible, efficient and collaborative optimization benchmarks [67.29240500171532]
  Benchopt is a framework to automate, reproduce and publish optimization benchmarks in machine learning. Benchopt simplifies benchmarking for the community by providing an off-the-shelf tool for running, sharing and extending experiments.
  arXiv  Detail & Related papers  (2022-06-27T16:19:24Z)
• Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program) [43.55295847227261]
  Reproducibility is obtaining similar results as presented in a paper or talk, using the same code and data (when available) In 2019, the Neural Information Processing Systems (NeurIPS) conference introduced a program, designed to improve the standards across the community for how we conduct, communicate, and evaluate machine learning research. In this paper, we describe each of these components, how they were deployed, as well as what we were able to learn from this initiative.
  arXiv  Detail & Related papers  (2020-03-27T02:16:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.