论文标题
在小组建议中解决极端冷启动问题
Addressing the Extreme Cold-Start Problem in Group Recommendation
论文作者
论文摘要
将项目推荐给一组用户的任务，又称小组建议，正在受到越来越多的关注。但是，在小组建议中，推荐系统固有的冷启动问题被放大了，因为在实践中，组和项目之间的相互作用数据极为稀缺。大多数现有的工作利用组与项目之间的关联以减轻数据稀缺问题。但是，现有方法不可避免地会在缺乏群体和项目之间的关联的极端冷启动场景中失败。因此，我们在小组推荐（Extre）中为Exreme Coldstar设计了一个小组推荐模型（Extre），适合极端冷启动方案。外推背后的基本思想是使用图形卷积神经网络的极限理论来建立组与项目之间的隐式关联，并且这些关联的推导不需要明确的交互数据，从而使其适用于冷启动方案。外推的训练过程取决于新定义的一致性和差异的可解释的概念，除了通常使用成对排名的常用负面抽样，这可以改善小组建议的性能。广泛的实验验证了所提出的模型外推的功效。
The task of recommending items to a group of users, a.k.a. group recommendation, is receiving increasing attention. However, the cold-start problem inherent in recommender systems is amplified in group recommendation because interaction data between groups and items are extremely scarce in practice. Most existing work exploits associations between groups and items to mitigate the data scarcity problem. However, existing approaches inevitably fail in extreme cold-start scenarios where associations between groups and items are lacking. For this reason, we design a group recommendation model for EXreme cold-star} in group REcommendation (EXTRE) suitable for the extreme cold start scenario. The basic idea behind EXTRE is to use the limit theory of graph convolutional neural networks to establish implicit associations between groups and items, and the derivation of these associations does not require explicit interaction data, making it suitable for cold start scenarios. The training process of EXTRE depends on the newly defined and interpretable concepts of consistency and discrepancy, other than commonly used negative sampling with pairwise ranking, which can improve the performance of the group recommendation. Extensive experiments validate the efficacy of the proposed model EXTRE.
