Time-Aware Adaptive Side Information Fusion for Sequential Recommendation

TL;DR

This paper introduces TASIF, a novel framework that effectively captures temporal dynamics, denoises user interaction data, and efficiently fuses side information to improve sequential recommendation performance.

Contribution

TASIF presents a unified, efficient approach combining temporal partitioning, adaptive filtering, and a guide-not-mix fusion architecture for enhanced sequential recommendation.

Findings

TASIF significantly outperforms state-of-the-art baselines on four datasets.

The framework maintains high training efficiency.

It effectively captures temporal patterns and denoises features.

Abstract

Incorporating item-side information, such as category and brand, into sequential recommendation is a well-established and effective approach for improving performance. However, despite significant advancements, current models are generally limited by three key challenges: they often overlook the fine-grained temporal dynamics inherent in timestamps, exhibit vulnerability to noise in user interaction sequences, and rely on computationally expensive fusion architectures. To systematically address these challenges, we propose the Time-Aware Adaptive Side Information Fusion (TASIF) framework. TASIF integrates three synergistic components: (1) a simple, plug-and-play time span partitioning mechanism to capture global temporal patterns; (2) an adaptive frequency filter that leverages a learnable gate to denoise feature sequences adaptively, thereby providing higher-quality inputs for subsequent fusion modules; and (3) an efficient adaptive side information fusion layer, this layer employs a "guide-not-mix" architecture, where attributes guide the attention mechanism without being mixed into the content-representing item embeddings, ensuring deep interaction while ensuring computational efficiency. Extensive experiments on four public datasets demonstrate that TASIF significantly outperforms state-of-the-art baselines while maintaining excellent efficiency in training. Our source code is available at https://github.com/jluo00/TASIF.