The Unreasonable Effectiveness of Data for Recommender Systems
In recommender systems, collecting, storing, and processing large-scale interaction data is increasingly costly in terms of time, energy, and computation, yet it remains unclear when additional data stops providing meaningful gains. This paper investigates how offline recommendation performance evolves as the size of the training dataset increases and whether a saturation point can be observed. We implemented a reproducible Python evaluation workflow with two established toolkits, LensKit and RecBole, included 11 large public datasets with at least 7 million interactions, and evaluated 10 tool-algorithm combinations. Using absolute stratified user sampling, we trained models on nine sampl...
| Authors | Youssef Abdou |
|---|---|
| Primary category | cs.IR |
| Published | 7 April 2026 |
| Last updated at source | 9 April 2026 |
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