提出高效线性模型TALE，用时间信息提升推荐精度。
Temporal Linear Item-Item Model for Sequential Recommendation
- 基于单目标增强与时间间隔加权，融合真实时间戳信息。
- 在5个数据集上领先18.71%，长尾物品推荐提升30.45%。
- 适合追求高效且需捕捉用户偏好变化的推荐系统场景。
在序列推荐中，神经模型虽表现优异但效率低下；而传统线性模型虽快却忽略真实时间信息，难以捕捉用户偏好随时间的变化。为此，本文提出一种新型线性序列推荐模型TALE，通过三个关键组件实现高效且精准的建模：(i) 单目标增强聚焦单一目标项，学习其时间相关性；(ii) 时间间隔感知加权利用实际时间戳判断项间关联强度；(iii) 趋势感知归一化反映项流行度的动态变化。实验证明，TALE在五个基准数据集上优于十种对比模型，最高提升达18.71%；对长尾物品的推荐效果提升最高达30.45%。代码已开源。
原文摘要 · Abstract (English)
In sequential recommendation (SR), neural models have been actively explored due to their remarkable performance, but they suffer from inefficiency inherent to their complexity. On the other hand, linear SR models exhibit high efficiency and achieve competitive or superior accuracy compared to neural models. However, they solely deal with the sequential order of items (i.e., sequential information) and overlook the actual timestamp (i.e., temporal information). It is limited to effectively capturing various user preference drifts over time. To address this issue, we propose a novel linear SR model, named TemporAl LinEar item-item model (TALE), incorporating temporal information while preserving training/inference efficiency, with three key components. (i) Single-target augmentation concentrates on a single target item, enabling us to learn the temporal correlation for the target item. (ii) Time interval-aware weighting utilizes the actual timestamp to discern the item correlation depending on time intervals. (iii) Trend-aware normalization reflects the dynamic shift of item popularity over time. Our empirical studies show that TALE outperforms ten competing SR models by up to 18.71% gains on five benchmark datasets. It also exhibits remarkable effectiveness in evaluating long-tail items by up to 30.45% gains. The source code is available at https://github.com/psm1206/TALE.
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