ConvFormer: Revisiting Token-mixers for Sequential User Modeling

Eric Wang, Jianxun Lian, Haoxuan Li, Mingqi Wu, Wanyue Xu, Jiajun Fan, Chaozhuo Li, Xing Xie

Primary Area: general machine learning (i.e., none of the above)

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Keywords: Sequential user modeling, Transformer, Token mixer

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TL;DR: Empirical criteria for devising effective token-mixers in Transformer-like sequential user models.

Abstract: Sequential user modeling is essential for building recommender systems, aiming to predict users' subsequent preferences based on their historical behavior. Despite the widespread success of the Transformer architecture in various domains, we observe that its self-attentive token mixer is outperformed by simpler strategies in the realm of sequential user modeling. This observation motivates our study, which aims to revisit and optimize the design of token mixers for this specific application. We start by examining the core building blocks of the self-attentive token mixer, identifying three empirically-validated criteria essential for designing effective token mixers in sequential user models. To validate the utility of these criteria, we develop ConvFormer, a streamlined modification to the Transformer architecture that satisfies the proposed criteria simultaneously. We also present an acceleration technique to handle the computational cost of processing long sequences. Experimental results on four public datasets reveal that even a simple model, when designed in accordance with the proposed criteria, can surpass various complex and delicate solutions, validating the efficacy of the proposed criteria.

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Submission Number: 7216