Adaptive popularity-debiased contrastive learning for long-tail recommendation: diagnosing failure mechanisms and bridging the accuracy-diversity gap

Abstract

Long-tail recommenders often over-fit popular items because the training log is highly skewed: a small head catalogue dominates both ranking gradients and the negative samples used by contrastive learning. This paper studies that failure mode under full-ranking evaluation and proposes
Adaptive Popularity-Debiased Contrastive Learning (A-PDCL)
, a lightweight training-time correction for graph contrastive recommendation. The core idea is simple: in the item-side InfoNCE denominator, popular in-batch negatives are down-weighted and tail negatives are up-weighted using smoothed inverse item degree. Rather than requiring a separate manual debiasing sweep for every dataset, A-PDCL sets the exponent automatically as


$$\beta _{\textrm{eff}}=\beta _{\max }\cdot \textrm{Gini}_{\textrm{batch}}$$



β
eff

=

β
max

·

Gini
batch





, so batches with stronger popularity concentration receive stronger correction. We evaluate A-PDCL on Yelp, Amazon Books, and MIND using a unified global-time-split, full-ranking protocol. Multi-seed results show that A-PDCL selects regime-appropriate correction (


$$\beta _{\textrm{eff}}\approx 0.75$$



β
eff

≈
0.75




on sparse Amazon Books and


$$\approx 0.59$$


≈
0.59




on dense Yelp), recovers catalogue coverage lost by standard contrastive training in the sparse regime, and closely matches tuned fixed-


$$\beta $$

β



PDCL without a per-dataset


$$\beta $$

β



search. To motivate the design, we also report controlled diagnostics of a text-gated knowledge-distillation variant and identify two dense-domain failure channels: gate-gradient collapse and self-normalized inverse propensity scoring (SNIPS) optimization instability. The resulting method and analysis give practical guidance on when popularity-debiased contrastive learning is useful, when fixed debiasing is preferable, and where true cold-start items remain outside the method’s scope.