SA-CAISR: Stage-Adaptive and Conflict-Aware Incremental Sequential Recommendation

TL;DR

SA-CAISR is a novel incremental sequential recommendation framework that efficiently updates models by selectively removing outdated knowledge, achieving state-of-the-art performance with minimal memory and computational costs.

Contribution

It introduces a buffer-free, stage-adaptive, conflict-aware method using Fisher-weighted knowledge screening for efficient incremental learning in SR.

Findings

Improves Recall@20 by 2.0% on average across datasets.

Reduces memory usage by 97.5%.

Reduces training time by 46.9%.

Abstract

Sequential recommendation (SR) aims to predict a user's next action by learning from their historical interaction sequences. In real-world applications, these models require periodic updates to adapt to new interactions and evolving user preferences. While incremental learning methods facilitate these updates, they face significant challenges. Replay-based approaches incur high memory and computational costs, and regularization-based methods often struggle to discard outdated or conflicting knowledge. To overcome these challenges, we propose SA-CAISR, a Stage-Adaptive and Conflict-Aware Incremental Sequential Recommendation framework. As a buffer-free framework, SA-CAISR operates using only the old model and new data, directly addressing the high costs of replay-based techniques. SA-CAISR introduces a novel Fisher-weighted knowledge-screening mechanism that dynamically identifies outdated knowledge by estimating parameter-level conflicts between the old model and new data, selectively removing obsolete knowledge while preserving compatible historical patterns. This dynamic balance between stability and adaptability allows our method to achieve state-of-the-art performance in incremental SR. Specifically, SA-CAISR improves Recall@20 by 2.0% on average across datasets, while reducing memory usage by 97.5% and training time by 46.9% compared to the best baseline. This efficiency allows real-world systems to rapidly update user profiles with minimal computational overhead, ensuring more timely and accurate recommendations.