Event recommendation is an essential means to enable people to find attractive upcoming social events, such as party, exhibition, and concert. While growing line of research has focused on suggesting events to individuals, making event recommendation for a group of users has not been well studied. In this paper, we aim to recommend upcoming events for a group of users. We formalize group recommendation as a ranking problem and propose a group event recommendation framework GERF based on learning-to-rank technique. Specifically, we first analyze different contextual influences on user's event attendance, and extract preference of user to event considering each contextual influence. Then, the preference scores of the users in a group are taken as the features for learning-to-rank to model the preference of the group. Moreover, a fast pairwise learning-to-rank algorithm, Bayesian group ranking, is proposed to learn ranking model for each group. Our framework is easily to incorporate additional contextual influences, and can be applied to other group recommendation scenarios. Extensive experiments have been conducted to evaluate the performance of GERF on two real-world datasets and demonstrate the appealing performance of our method on both accuracy and time efficiency.

中文翻译：

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GERF：基于Learning-to-Rank的群体事件推荐框架

事件推荐是让人们能够找到有吸引力的即将到来的社交活动，如派对、展览和音乐会的重要手段。虽然越来越多的研究集中在向个人建议事件上，但尚未对为一组用户进行事件推荐进行深入研究。在本文中，我们旨在为一组用户推荐即将发生的事件。我们将群体推荐形式化为一个排序问题，并提出了一个基于学习排序技术的群体事件推荐框架 GERF。具体来说，我们首先分析不同的上下文影响对用户事件出席的影响，并考虑每个上下文影响来提取用户对事件的偏好。然后，将群组中用户的偏好分数作为学习排序的特征，以对群组的偏好进行建模。而且，提出了一种快速成对学习排序算法，贝叶斯组排序，用于学习每个组的排序模型。我们的框架很容易结合额外的上下文影响，并且可以应用于其他组推荐场景。已经进行了广泛的实验来评估 GERF 在两个真实世界数据集上的性能，并证明了我们的方法在准确性和时间效率方面的吸引力。