aiqbench analysis
This research identifies "residual dominance" as the structural mechanism causing Transformer-based recommenders like SASRec to over-rely on the most recent user interaction during inference. By manipulating residual scaling, the authors demonstrate a direct trade-off between this localization effect and the model's ability to utilize earlier contextual information for accurate predictions. The findings offer a concrete diagnostic tool for improving sequential recommendation systems by mitigating extreme recency bias without sacrificing overall representational capacity.
Residual Dominance as a Structural Account of Last-Item Reliance in Causal Self-Attention Recommenders
arXiv:2608.14021v1 Announce Type: new Abstract: Transformer-based sequential recommenders with causal self-attention often rely heavily on the most recent interaction at inference time, but how this behavior is structurally expressed in the representation used for prediction remains unclear. We combine prediction-time diagnostics with norm-based analysis of the full attention block. First, we show that SASRec-style models exhibit highly localized last-item reliance. We then find that, although self-attention aggregates contextual information, residual addition sharply shifts the full-block representation toward same-position contributions, which we term residual dominance. To probe this interpretation, we use inference-time residual scaling as a controlled diagnostic intervention. Changing the residual strength induces a monotonic trade-off between structural mixing and last-item reliance, while reducing residual strength recovers a subset of final-position misses for which representations at non-final positions already rank the ground-truth item correctly. Our results provide a structural account linking extreme last-item reliance to residual dominance at inference time. The code is publicly available.
Abstract:Transformer-based sequential recommenders with causal self-attention often rely heavily on the most recent interaction at inference time, but how this behavior is structurally expressed in the representation used for prediction remains unclear. We combine prediction-time diagnostics with norm-based analysis of the full attention block. First, we show that SASRec-style models exhibit highly localized last-item reliance. We then find that, although self-attention aggregates contextual information, residual addition sharply shifts the full-block representation toward same-position contributions, which we term residual dominance. To probe this interpretation, we use inference-time residual scaling as a controlled diagnostic intervention. Changing the residual strength induces a monotonic trade-off between structural mixing and last-item reliance, while reducing residual strength recovers a subset of final-position misses for which representations at non-final positions already rank the ground-truth item correctly. Our results provide a structural account linking extreme last-item reliance to residual dominance at inference time. The code is publicly available.
11 pages, 6 figures. Accepted at the 20th ACM Conference on Recommender Systems (RecSys'26)
Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
arXiv:2608.14021 [cs.AI]
(or arXiv:2608.14021v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.14021 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 261 words
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