In general, recommender systems are designed to provide personalized items to a user. But in few cases, items are recommended for a group, and the challenge is to aggregate the individual user preferences to infer the recommendation to a group. It is also important to consider the similarity of characteristics among the members of a group to generate a better recommendation. Members of an automatically identified group will have similar characteristics, and reaching a consensus with a decision-making process is preferable in this case. It requires users-items and their rating interactions over a utility matrix to auto-detect the groups in group recommendations. We may not overlook other intrinsic information to form a group. The textual information also plays a pivotal role in user clustering. In this paper, we auto-detect the groups based on the textual similarity of the metadata (review texts). We consider the order in user preferences in our models. We have conducted extensive experiments over two real-world datasets to check the efficacy of the proposed models. We have also conducted a competitive comparison with a baseline model to show the improvements in the quality of recommendations.

中文翻译：

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基于文本相似性的组推荐自动检测组

通常，推荐系统旨在为用户提供个性化的项目。但在少数情况下，项目是为一组推荐的，挑战在于聚合个人用户偏好以推断对组的推荐。考虑组成员之间特征的相似性以生成更好的推荐也很重要。自动识别的组的成员将具有相似的特征，在这种情况下，最好通过决策过程达成共识。它需要用户-项目及其在效用矩阵上的评分交互来自动检测组推荐中的组。我们可能不会忽略其他内在信息来形成一个群体。文本信息在用户聚类中也起着关键作用。在本文中，我们根据元数据（评论文本）的文本相似性自动检测组。我们在模型中考虑用户偏好的顺序。我们对两个真实世界的数据集进行了大量实验，以检查所提出模型的有效性。我们还与基准模型进行了竞争比较，以显示推荐质量的改进。