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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.07319 |
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| _version_ | 1866914029260242944 |
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| author | Shi, Yunxiao Yang, Shuo Zhang, Haimin Wang, Li Wang, Yongze Wu, Qiang Xu, Min |
| author_facet | Shi, Yunxiao Yang, Shuo Zhang, Haimin Wang, Li Wang, Yongze Wu, Qiang Xu, Min |
| contents | Neural Collaborative Filtering models are widely used in recommender systems but are typically trained under static settings, assuming fixed data distributions. This limits their applicability in dynamic environments where user preferences evolve. Incremental learning offers a promising solution, yet conventional methods from computer vision or NLP face challenges in recommendation tasks due to data sparsity and distinct task paradigms. Existing approaches for neural recommenders remain limited and often lack generalizability. To address this, we propose MEGG, Replay Samples with Maximally Extreme GGscore, an experience replay based incremental learning framework. MEGG introduces GGscore, a novel metric that quantifies sample influence, enabling the selective replay of highly influential samples to mitigate catastrophic forgetting. Being model-agnostic, MEGG integrates seamlessly across architectures and frameworks. Experiments on three neural models and four benchmark datasets show superior performance over state-of-the-art baselines, with strong scalability, efficiency, and robustness. Implementation will be released publicly upon acceptance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07319 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Shi, Yunxiao Yang, Shuo Zhang, Haimin Wang, Li Wang, Yongze Wu, Qiang Xu, Min Information Retrieval Artificial Intelligence Neural Collaborative Filtering models are widely used in recommender systems but are typically trained under static settings, assuming fixed data distributions. This limits their applicability in dynamic environments where user preferences evolve. Incremental learning offers a promising solution, yet conventional methods from computer vision or NLP face challenges in recommendation tasks due to data sparsity and distinct task paradigms. Existing approaches for neural recommenders remain limited and often lack generalizability. To address this, we propose MEGG, Replay Samples with Maximally Extreme GGscore, an experience replay based incremental learning framework. MEGG introduces GGscore, a novel metric that quantifies sample influence, enabling the selective replay of highly influential samples to mitigate catastrophic forgetting. Being model-agnostic, MEGG integrates seamlessly across architectures and frameworks. Experiments on three neural models and four benchmark datasets show superior performance over state-of-the-art baselines, with strong scalability, efficiency, and robustness. Implementation will be released publicly upon acceptance. |
| title | MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2509.07319 |