English(EN)RecRec: Recursive Refinement for Sequential Recommendation
新AI模型RecRec和NAILS通过递归式精炼和规范对齐增强推荐系统
作者PulseAugur 编辑部·[9 个来源]·
研究人员推出了两种增强推荐系统的新方法。第一种，RecRec，采用递归式精炼来用紧凑的潜在状态对用户偏好进行建模，在效率和准确性方面优于现有模型。第二种，NAILS（通过内部标签偏移对推荐系统进行规范对齐），提供了一种在不重新训练现有系统的情况下，将推荐输出与期望的属性分布（如公平性或多样性）对齐的方法。此外，SISA-Rec将语义项信息集成到基于Transformer的模型中，以提高性能，尤其是在稀疏和冷启动场景下。
AI
Sequential recommender systems rely on a single forward pass to encode user interaction histories and predict the next item. Increasing inference-time computation through latent reasoning, with the model proceeding step by step before the final prediction, has been recently explo…
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We introduce NAILS (Normative Alignment of Recommender Systems via Internal Label Shift), a simple and scalable method for aligning recommendation outputs with target distributions over item-level attributes, such as categories. Recommender systems optimized solely for user engag…
Sequential recommender systems typically infer user preferences through single-pass encoding of interaction histories without iterative refinement, relying on increasingly deep architectures to capture complex patterns. In this work, we revisit sequential recommendation from a re…
Sequential recommender systems typically infer user preferences through single-pass encoding of interaction histories without iterative refinement, relying on increasingly deep architectures to capture complex patterns. In this work, we revisit sequential recommendation from a re…
Sequential recommender systems typically infer user preferences through single-pass encoding of interaction histories without iterative refinement, relying on increasingly deep architectures to capture complex patterns. In this work, we revisit sequential recommendation from a re…
arXiv:2607.11168v1 Announce Type: new Abstract: Recommendation systems help users recommend relevant items from a large collection of choices. Present work on transformer-based sequential recommendation learns user preferences from interaction logs, but it mostly focuses on item …
Recommendation systems help users recommend relevant items from a large collection of choices. Present work on transformer-based sequential recommendation learns user preferences from interaction logs, but it mostly focuses on item identifiers and doesn't fully use the semantic m…
