Data Processing for Offline Evaluation in Recommender Systems: a Survey
Offline evaluation is the dominant experimental paradigm in recommender systems research, enabling reproducible and cost-effective comparisons on historical interaction data. Yet, while considerable attention has been devoted to recommendation models and evaluation methodologies, the data processing decisions that precede model training have received less scrutiny. These decisions determine the information available to recommendation algorithms and can affect the comparability and reproducibility of experimental results. This survey provides a systematic, cross-domain characterisation of data processing practices for the offline evaluation of recommender systems. We examine the data-centr...
| Authors | Alberto Carlo Maria Mancino, Angela Di Fazio, Danilo Danese et al. |
|---|---|
| Primary category | cs.IR |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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