通过双路信息过滤与融合，提升序列推荐在稀疏场景下的精准度
DIFF: Dual Side-Information Filtering and Fusion for Sequential Recommendation
- 将物品序列转至频域，过滤短期兴趣噪声
- 结合早期与中期融合，捕捉物品属性与ID的多重关系
- 在四个数据集上召回率最高提升14.1%，适合冷启动推荐
侧信息集成的序列推荐（SISR）通过辅助物品信息推断用户隐含偏好，在交互稀疏和冷启动场景中表现优异。然而现有方法面临两大挑战：(i) 无法有效去除物品序列中的噪声信号；(ii) 侧信息整合潜力未被充分挖掘。为此，我们提出新型SISR模型DIFF（Dual Side-Information Filtering and Fusion），采用基于频率的噪声过滤与双路多序列融合策略。具体地，将物品序列转换至频域以滤除用户兴趣的短期波动噪声；随后结合早期与中间层融合，捕捉物品ID与属性间的多样化关联。得益于创新的过滤与融合机制，DIFF在学习序列中细微复杂的物品相关性方面更具鲁棒性。在四个基准数据集上，DIFF相较当前最优SISR模型，Recall@20最高提升14.1%，NDCG@20最高提升12.5%。
原文摘要 · Abstract (English)
Side-information Integrated Sequential Recommendation (SISR) benefits from auxiliary item information to infer hidden user preferences, which is particularly effective for sparse interactions and cold-start scenarios. However, existing studies face two main challenges. (i) They fail to remove noisy signals in item sequence and (ii) they underutilize the potential of side-information integration. To tackle these issues, we propose a novel SISR model, Dual Side-Information Filtering and Fusion (DIFF), which employs frequency-based noise filtering and dual multi-sequence fusion. Specifically, we convert the item sequence to the frequency domain to filter out noisy short-term fluctuations in user interests. We then combine early and intermediate fusion to capture diverse relationships across item IDs and attributes. Thanks to our innovative filtering and fusion strategy, DIFF is more robust in learning subtle and complex item correlations in the sequence. DIFF outperforms state-of-the-art SISR models, achieving improvements of up to 14.1% and 12.5% in Recall@20 and NDCG@20 across four benchmark datasets.
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