商品价格影响群体决策中用户影响力，模型据此动态调整注意力。
Price-guided user attention in large-scale E-commerce group recommendation
- 根据商品价格动态调整用户注意力权重，提升群体推荐精度。
- 在真实电商数据上，点击率和均方误差均优于现有方法。
- 可无缝集成到任意基于注意力的群组推荐系统中。
现有群组推荐系统使用注意力机制识别对群体决策影响最大的关键用户。我们在一个真实的电商数据集上分析了一个广泛使用的群组推荐模型的用户注意力得分，发现商品价格和用户交互历史显著影响关键用户的选取：当商品价格较低时，交互历史丰富的用户更具影响力；而随着价格升高，其影响力下降。基于此观察，我们提出一种新型群组推荐方法，将商品价格作为引导用户聚合的因子。模型采用自适应Sigmoid函数根据商品价格调节输出逻辑值，提升用户聚合的准确性。该模型可在具备价格信息的前提下，无缝嵌入任何基于注意力的群组推荐系统。我们在公开基准数据集和真实世界数据集上评估了模型性能，并与多种前沿群组推荐方法对比。结果表明，所提出的基于价格引导的用户注意力方法在命中率（hit ratio）和均方误差（mean square error）方面均优于当前最优方法。
原文摘要 · Abstract (English)
Existing group recommender systems utilize attention mechanisms to identify critical users who influence group decisions the most. We analyzed user attention scores from a widely-used group recommendation model on a real-world E-commerce dataset and found that item price and user interaction history significantly influence the selection of critical users. When item prices are low, users with extensive interaction histories are more influential in group decision-making. Conversely, their influence diminishes with higher item prices. Based on these observations, we propose a novel group recommendation approach that incorporates item price as a guiding factor for user aggregation. Our model employs an adaptive sigmoid function to adjust output logits based on item prices, enhancing the accuracy of user aggregation. Our model can be plugged into any attention-based group recommender system if the price information is available. We evaluate our model's performance on a public benchmark and a real-world dataset. We compare it with other state-of-the-art group recommendation methods. Our results demonstrate that our price-guided user attention approach outperforms the state-of-the-art methods in terms of hit ratio and mean square error.
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