Group recommendations aim to suggest items to a group of users based on their preferences. Many group recommendations often consider various factors to calculate the influence of each group member and then assign weights to complete the group recommendation, aiming to maximize group member satisfaction. However, most group recommendations tend to focus more on the calculation process of group members’ influence while ignoring the process of assigning their weights, which may lead to low group members’ satisfaction. To solve this problem, a novel group recommendation approach called GroupRec_D is proposed to assign weights of users scientifically and reasonably based on data mining and DEMATEL technique. To demonstrate the availability and effectiveness of GroupRec_D, we conduct extensive experiments on the MovieLens 100k dataset and use three evaluation metrics including GSM, RECALL, and nDCG to evaluate the approach. Experimental results demonstrate that GroupRec_D outperforms other comparison approaches.

群组推荐旨在根据用户的偏好向他们推荐项目。很多群推荐往往会考虑各种因素来计算每个群成员的影响力，然后分配权重来完成群推荐，旨在最大化群成员的满意度。然而，大多数群体推荐往往更多地关注群体成员影响力的计算过程，而忽略了分配权重的过程，这可能导致群体成员的满意度较低。针对这一问题，提出了一种基于数据挖掘和DEMATEL技术的群组推荐新方法GroupRec _D ，科学合理地为用户分配权重。证明的可用性和有效性GroupRec _D，我们在MovieLens 100k数据集上进行了大量实验，并使用包括GSM、RECALL和nDCG在内的三个评估指标来评估该方法。实验结果表明GroupRec _D优于其他比较方法。