Penerapan Empirical-Bayes pada Sistem Peringkat Produk E-Commerce
DOI:
https://doi.org/10.37859/jf.v15i3.10494
Abstract
This study examines the application of Empirical Bayes (EB) smoothing for product ranking in e-commerce platforms characterized by sparse sales signals and highly skewed transaction distributions. Under these conditions, top lists tend to fluctuate when rankings rely solely on raw cumulative sales, particularly for long-tail products; therefore, a method that balances population-level information with item-level evidence is required to produce more consistent top-k rankings. The method models purchase counts using a Gamma–Poisson framework, where a global prior is estimated from the overall data and item-level posteriors are updated so that the posterior mean serves as a smoothed popularity score. Experiments are conducted on real product catalogs (smartphones and laptops) augmented with a 12-week sales simulation featuring mild seasonality and promotional noise, and EB is compared against a naive baseline that ranks items by raw cumulative units sold under a rolling, week-by-week evaluation. Results show that EB improves NDCG@5 and NDCG@10 while reducing week-to-week Top-10 churn relative to the baseline, with the most notable gains observed for low-signal and long-tail items because shrinkage dampens extreme rank swings caused by sparse observations. Overall, EB smoothing is effective in stabilizing top-k product rankings for listing interfaces and administrative dashboards, and it can be extended through time-decayed priors and the incorporation of contextual features such as price and category to further improve ranking accuracy
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