Many online services allow users to participate in various group activities such as online meeting or group buying and thus need to provide user groups with services that they are interested. The group recommender systems emerge as required and provide personalized services for various online user groups. Data sparsity is an important issue in group recommender systems, since even fewer group-item interactions are observed. Transfer learning has been one efficient tool to alleviate the data sparsity issue in recommender systems for individual users, but have not been utilized for group recommendation. Moreover, the group and the group members have complex and mutual relationships with each other, which exacerbates the difficulty in modelling the preferences of both a group and its members for recommendation. Therefore, group recommender systems face three main challenges that may significantly impact its quality and accuracy: (1) taking consideration of group member relationship and their interactions in modelling user and group preferences; (2) ensuring latent feature spaces between the users and groups are maximally matched; and (3) constructing a deep group recommendation method that both the individual user and group domains can benefit from a knowledge exchange. Hence, in this paper, we propose a deep adversarial group recommendation method, called DA-GR. User feature are separated into two subspaces to ensure only consistent group members’ feature knowledge can be extracted and shared with group preference modelling. Adversarial learning is used to effectively transfer consistent knowledge from individual user interactions to the group interaction domain through the bridge of group-user relationships. Extensive experiments, which demonstrate the effectiveness and superiority of our proposal, providing accurate recommendation for both individual users and groups, are conducted on public datasets. The source code of DA-GR is in https://github.com/ccnu-mathits/DA-GR.

许多在线服务允许用户参与各种群体活动，例如在线会议或团购，因此需要为用户群体提供他们感兴趣的服务。群组推荐系统应运而生，为各种在线用户群提供个性化服务。数据稀疏性是群组推荐系统中的一个重要问题，因为观察到的群组-项目交互甚至更少。迁移学习一直是缓解个人用户推荐系统中数据稀疏性问题的有效工具，但尚未用于群组推荐。此外，群体和群体成员之间具有复杂的相互关系，这加剧了对群体及其成员的偏好进行建模以进行推荐的难度。所以，群组推荐系统面临三个可能显着影响其质量和准确性的主要挑战：（1）在建模用户和群组偏好时考虑群组成员关系及其交互；(2) 确保用户和组之间的潜在特征空间最大化匹配；(3) 构建一个深度群体推荐方法，个人用户和群体领域都可以从知识交流中获益。因此，在本文中，我们提出了一种称为 DA-GR 的深度对抗组推荐方法。用户特征被分成两个子空间，以确保只有一致的组成员的特征知识才能被提取并与组偏好建模共享。对抗性学习用于通过组-用户关系的桥梁将一致的知识从个人用户交互有效地转移到组交互域。在公共数据集上进行了广泛的实验，证明了我们提议的有效性和优越性，为个人用户和群体提供了准确的推荐。DA-GR 的源代码在 https://github.com/ccnu-mathits/DA-GR。