Reproduce “Candidate Set Sampling for Evaluating Top-N Recommendation” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Ngozi Ihemelandu, Michael D. Ekstrand
Research from Boise State University empirically demonstrates that uniform sampling of decoy items in offline recommender system evaluation exacerbates popularity bias, which can be mitigated by popularity-weighted sampling. The study also finds that metrics derived from sampled candidate sets can provide better estimates of true system effectiveness compared to traditional full candidate set evaluations on biased observed data.
Key insights
- Uniform sampling of decoy items significantly exacerbates popularity bias in offline evaluation, especially on datasets with highly skewed popularity distributions.
- Popularity-weighted sampling of decoys effectively mitigates this bias by forcing algorithms to differentiate between relevant and irrelevant popular items.
- Full candidate sets, when evaluated on observed (biased) data, may systematically underestimate the true effectiveness of recommender systems, while sampled candidate sets can provide better estimates.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.