Recommender systems are widely used in big information-based companies such as Google, Twitter, LinkedIn, and Netflix. A recommender system deals with the problem of information overload by filtering important information fragments according to users' preferences. In light of the increasing success of deep learning, recent studies have proved the benefits of using deep learning in various recommendation tasks. However, most proposed techniques only aim to target individuals, which cannot be efficiently applied in group recommendation. In this paper, we propose a deep learning architecture to solve the group recommendation problem. On the one hand, as different individual preferences in a group necessitate preference trade-offs in making group recommendations, it is essential that the recommendation model can discover substitutes among user behaviors. On the other hand, it has been observed that a user as an individual and as a group member behaves differently. To tackle such problems, we propose using an attention mechanism to capture the impact of each user in a group. Specifically, our model automatically learns the influence weight of each user in a group and recommends items to the group based on its members' weighted preferences. We conduct extensive experiments on four datasets. Our model significantly outperforms baseline methods and shows promising results in applying deep learning to the group recommendation problem.
翻译：推荐人系统在谷歌、Twitter、LinkedIn和Netflix等大型信息型公司中广泛使用。 推荐人系统根据用户的偏好,通过过滤重要信息碎片,处理信息过量问题。 鉴于深层学习日益成功,最近的研究证明,在各种建议任务中运用深层学习的好处日益明显。然而,大多数拟议的技术仅针对个人,无法有效地应用到集体建议中。在本文件中,我们提议了一个深层次的学习结构来解决集团建议问题。一方面,由于一个集团中不同的个人偏好需要在小组建议中作出偏好取舍,建议模式必须能够发现用户行为中的替代物。另一方面,发现用户个人和群体成员的行为不同。为了解决这些问题,我们建议使用关注机制来捕捉每个用户在集团中的影响。具体地说,我们的模型自动了解每个用户在集团中的影响力,并根据其成员的加权偏好程度向集团推荐项目。我们在四个数据集中进行广泛的实验。我们在四个数据集中进行广泛的实验。我们的模式大大超越了小组的基线方法,在小组中显示有希望的结果。