mlsanityData to 7 Oct 2026

Widespread Flaws in Offline Evaluation of Recommender Systems

arXiv 2307.14951 ↗First posted 27 Jul 2023cs.IRcited by 21

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Abstract (arXiv)

Even though offline evaluation is just an imperfect proxy of online performance -- due to the interactive nature of recommenders -- it will probably remain the primary way of evaluation in recommender systems research for the foreseeable future, since the proprietary nature of production recommenders prevents independent validation of A/B test setups and verification of online results. Therefore, it is imperative that offline evaluation setups are as realistic and as flawless as they can be. Unfortunately, evaluation flaws are quite common in recommender systems research nowadays, due to later works copying flawed evaluation setups from their predecessors without questioning their validity. In the hope of improving the quality of offline evaluation of recommender systems, we discuss four of these widespread flaws and why researchers should avoid them.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jul 2023.

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Citing papers per year: 2024: 5; 2025: 12; 2026: 4.

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  1. 2024Does It Look Sequential? An Analysis of Datasets for Evaluation of Sequential Recommendationscited by 15
  2. 2025Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenderscited by 12
  3. 2024Autoregressive Generation Strategies for Top-K Sequential Recommendationscited by 5
  4. 2024Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation Modelscited by 4
  5. 2024Building a Scalable, Effective, and Steerable Search and Ranking Platformcited by 3
  6. 2025eSASRec: Enhancing Transformer-based Recommendations in a Modular Fashioncited by 3
  7. 2024Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matterscited by 2
  8. 2025Are We Really Measuring Progress? Transferring Insights from Evaluating Recommender Systems to Temporal Link Predictioncited by 2
  9. 2025Recommendation Is a Dish Better Served Warmcited by 2
  10. 2025Let It Go? Not Quite: Addressing Item Cold Start in Sequential Recommendations with Content-Based Initializationcited by 1
  11. 2025Balancing Accuracy and Novelty with Sub-Item Popularitycited by 1
  12. 2025We're Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Latercited by 1
9 more
  1. 2025ORBIT -- Open Recommendation Benchmark for Reproducible Research with Hidden Testscited by 1
  2. 2026ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatchcited by 1
  3. 2025Compositions of Variant Experts for Integrating Short-Term and Long-Term Preferencescited by 0
  4. 2025Identifying Offline Metrics that Predict Online Impact: A Pragmatic Strategy for Real-World Recommender Systemscited by 0
  5. 2025Semantic IDs for Music Recommendationcited by 0
  6. 2025Industry Insights from Comparing Deep Learning and GBDT Models for E-Commerce Learning-to-Rankcited by 0
  7. 2026SplitLight: An Exploratory Toolkit for Recommender Systems Datasets and Splitscited by 0
  8. 2026Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spotscited by 0
  9. 2026Residual Dominance as a Structural Account of Last-Item Reliance in Causal Self-Attention Recommenderscited by 0