Session-based recommendation (SBR) aims to predict anonymous users' next interaction based on their interaction sessions. In the practical recommendation scenario, low-exposure items constitute the majority of interactions, creating a long-tail distribution that severely compromises recommendation diversity. Existing approaches attempt to address this issue by promoting tail items but incur accuracy degradation, exhibiting a "see-saw" effect between long-tail and accuracy performance. We attribute such conflict to session-irrelevant noise within the tail items, which existing long-tail approaches fail to identify and constrain effectively. To resolve this fundamental conflict, we propose \textbf{HID} (\textbf{H}ybrid \textbf{I}ntent-based \textbf{D}ual Constraint Framework), a plug-and-play framework that transforms the conventional "see-saw" into "win-win" through introducing the hybrid intent-based dual constraints for both long-tail and accuracy. Two key innovations are incorporated in this framework: (i) \textit{Hybrid Intent Learning}, where we reformulate the intent extraction strategies by employing attribute-aware spectral clustering to reconstruct the item-to-intent mapping. Furthermore, discrimination of session-irrelevant noise is achieved through the assignment of the target and noise intents to each session. (ii) \textit{Intent Constraint Loss}, which incorporates two novel constraint paradigms regarding the \textit{diversity} and \textit{accuracy} to regulate the representation learning process of both items and sessions. These two objectives are unified into a single training loss through rigorous theoretical derivation. Extensive experiments across multiple SBR models and datasets demonstrate that HID can enhance both long-tail performance and recommendation accuracy, establishing new state-of-the-art performance in long-tail recommender systems.

翻译：会话推荐旨在根据匿名用户的交互会话来预测其下一次交互行为。在实际推荐场景中，低曝光项目构成了交互行为的主体，形成了严重损害推荐多样性的长尾分布。现有方法试图通过提升尾部项目来解决这一问题，但会导致推荐精度下降，呈现出长尾性能与精度表现之间的"跷跷板"效应。我们将这种冲突归因于尾部项目中存在的会话无关噪声，而现有长尾方法未能有效识别并约束此类噪声。为解决这一根本性冲突，我们提出\textbf{HID}（基于\textbf{H}ybrid \textbf{I}ntent的\textbf{D}ual Constraint Framework）框架——一种即插即用的推荐框架，通过引入面向长尾性能与推荐精度的混合意图双重约束，将传统的"跷跷板"困境转化为"双赢"局面。该框架包含两项核心创新：(i) \textit{混合意图学习}：通过采用属性感知谱聚类重构项目到意图的映射关系，重新构建意图提取策略。此外，通过为每个会话分配目标意图与噪声意图，实现对会话无关噪声的判别。(ii) \textit{意图约束损失}：引入关于\textit{多样性}与\textit{精度}的两种新型约束范式，以规范项目与会话的表征学习过程。通过严格的理论推导，这两个目标被统一为单一训练损失函数。在多个会话推荐模型与数据集上的大量实验表明，HID框架能够同时提升长尾性能与推荐精度，在长尾推荐系统中建立了新的性能标杆。