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

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 β eff=β ₘₐₓ· Ginibatch 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 ( β eff≈ 0.75 β eff ≈ 0.75 on sparse Amazon Books and ≈ 0.59 ≈ 0.59 on dense Yelp), recovers catalogue coverage lost by standard contrastive training in the sparse regime, and closely matches tuned fixed- β β PDCL without a per-dataset β 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.