Group recommender system (GRS) is the gradually prospering type of recommender system (RS) which tends to provide recommendations for the group of users rather than the individual. Most of the existing GRS obtain group preferences using equal weighing of the individual preferences, ignoring the relationship among group members within the group. But this is not a practical scenario because each member has different behavior. Therefore, in this article, we introduce a multiagent based negotiation mechanism between agents, each of them acts in favor of one group member. The proposed negotiation protocol allows agents to accept or discard a part of the offer based on trust and distrust among users, which gives more agility to the negotiation process. Further, we use memory for each agent in the group that records the previously proposed offers for that agent. The efficiency of trust-distrust enhanced GRSs is compared with traditional techniques and the outcomes of computational experiments confirm the supremacy of our proposed models over baseline GRSs techniques.

团体推荐系统（GRS）是一种逐渐兴旺的推荐系统（RS），它倾向于为用户组而不是个人提供推荐。现有的大多数GRS都使用对各个首选项的权重相等的权重来获得群组首选项，而忽略了该群组内各群组成员之间的关系。但这不是实际情况，因为每个成员都有不同的行为。因此，在本文中，我们介绍了一种基于多代理的代理之间的协商机制，每个代理都代表一个组成员。提议的协商协议允许代理基于用户之间的信任和不信任来接受或丢弃报价的一部分，这为协商过程提供了更大的灵活性。进一步，我们会为记录该代理商先前提议的报价的组中的每个代理商使用内存。信任-不信任增强型GRS的效率与传统技术进行了比较，计算实验的结果证实了我们提出的模型优于基线GRSs技术的优势。