重审自注意力推荐模型，发现可优化细节并提出新实验方法。
Revisiting Self-Attentive Sequential Recommendation
- 分析经典自注意力模型的实现细节，识别潜在优化点。
- 提出新实验范式，提升结果可复现性与可比性。
- 适合对序列推荐机制优化感兴趣的学者和工程师。
推荐系统在在线服务中无处不在，用于提升个性化体验。许多序列推荐模型被部署于这些系统中。使用Transformer解码器作为序列推荐器的方法虽已提出多年，仍是近期工作的强大基线。然而，此类推荐模型在扩展性上远不如语言模型。经典自注意力序列推荐模型中的诸多实现细节值得重新审视，新的实验设计可能带来新发现，且无需改变原有模型结构——这也是以往研究的重点。本文旨在揭示这些细节，并提出面向未来研究的新实验方法，以期激发该领域进一步探索与创新。
原文摘要 · Abstract (English)
Recommender systems are ubiquitous in on-line services to drive businesses. And many sequential recommender models were deployed in these systems to enhance personalization. The approach of using the transformer decoder as the sequential recommender was proposed years ago and is still a strong baseline in recent works. But this kind of sequential recommender model did not scale up well, compared to language models. Quite some details in the classical self-attentive sequential recommender model could be revisited, and some new experiments may lead to new findings, without changing the general model structure which was the focus of many previous works. In this paper, we show the details and propose new experiment methodologies for future research on sequential recommendation, in hope to motivate further exploration to new findings in this area.
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