Temporal Linear Item-Item Model for Sequential Recommendation

TL;DR

TALE is a linear sequential recommendation model that effectively incorporates temporal information to improve accuracy and long-tail item evaluation while maintaining high efficiency.

Contribution

It introduces a novel linear model with temporal components, addressing the limitations of existing linear models by capturing temporal dynamics efficiently.

Findings

Outperforms ten competing models by up to 18.71% on benchmark datasets.

Significantly improves long-tail item recommendation by up to 30.45%.

Maintains high efficiency comparable to traditional linear models.

Abstract

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.