Group Recommendation (GR), which aims to recommend items to groups of users, has become a promising and practical direction for recommendation systems. This paper points out two issues of the state-of-the-art GR models. (1) The pre-defined and fixed number of user groups is inadequate for real-time industrial recommendation systems, where the group distribution can shift dynamically. (2) The training schema of existing GR methods is supervised, necessitating expensive user-group and group-item labels, leading to significant annotation costs. To this end, we present a novel unsupervised group recommendation framework named \underline{I}dentify \underline{T}hen \underline{R}ecommend (\underline{ITR}), where it first identifies the user groups in an unsupervised manner even without the pre-defined number of groups, and then two pre-text tasks are designed to conduct self-supervised group recommendation. Concretely, at the group identification stage, we first estimate the adaptive density of each user point, where areas with higher densities are more likely to be recognized as group centers. Then, a heuristic merge-and-split strategy is designed to discover the user groups and decision boundaries. Subsequently, at the self-supervised learning stage, the pull-and-repulsion pre-text task is proposed to optimize the user-group distribution. Besides, the pseudo group recommendation pre-text task is designed to assist the recommendations. Extensive experiments demonstrate the superiority and effectiveness of ITR on both user recommendation (e.g., 22.22\% NDCG@5 $\uparrow$) and group recommendation (e.g., 22.95\% NDCG@5 $\uparrow$). Furthermore, we deploy ITR on the industrial recommender and achieve promising results.

翻译：群组推荐（GR）旨在向用户群组推荐物品，已成为推荐系统领域一个前景广阔且具有实用价值的研究方向。本文指出了当前最先进GR模型存在的两个问题：（1）预先定义且固定数量的用户群组无法适应实时工业推荐系统，因为群组分布可能动态变化。（2）现有GR方法的训练范式是监督式的，需要昂贵的用户-群组和群组-物品标注，导致显著的标注成本。为此，我们提出了一种新颖的无监督群组推荐框架——识别后推荐（ITR），该框架首先以无监督方式识别用户群组（甚至无需预先设定群组数量），随后设计两个前置任务进行自监督群组推荐。具体而言，在群组识别阶段，我们首先估计每个用户点的自适应密度，其中密度较高的区域更可能被识别为群组中心。接着，设计一种启发式的合并-分裂策略来发现用户群组及其决策边界。随后，在自监督学习阶段，提出推拉前置任务以优化用户-群组分布。此外，设计了伪群组推荐前置任务以辅助推荐。大量实验证明了ITR在用户推荐（例如NDCG@5提升22.22%↑）和群组推荐（例如NDCG@5提升22.95%↑）方面的优越性与有效性。进一步地，我们将ITR部署于工业推荐系统并取得了显著成效。