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

2026-02-09

AI Summary

This work addresses the challenges of high memory overhead, computational cost, and the difficulty of discarding outdated or conflicting knowledge in incremental updates for sequential recommendation systems. To this end, the authors propose a buffer-free incremental learning framework that eliminates the need to store historical data, enabling efficient model updates using only the previous model and incoming new data. A key innovation is the introduction of a Fisher information–based, parameter-level knowledge filtering mechanism that dynamically identifies and removes outdated knowledge conflicting with new data while preserving compatible historical patterns. Extensive experiments demonstrate that the proposed method consistently outperforms existing approaches, achieving average improvements of +2.0% in Recall@20, +1.2% in MRR@20, and +1.4% in NDCG@20 across multiple benchmark datasets, alongside a 97.5% reduction in memory usage and a 46.9% decrease in training time.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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approaches incur high memory and computational costs, and regularization-based methods often struggle to discard outdated or conflicting knowledge.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.

Problem

• Sequential Recommendation
• Incremental Learning
• Knowledge Conflict
• Model Adaptation
• User Preference Evolution

Innovation

• incremental sequential recommendation
• buffer-free learning
• Fisher-weighted knowledge screening
• conflict-aware adaptation
• stage-adaptive framework