Abstract
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 GroupRecD is proposed to assign weights of users scientifically and reasonably based on data mining and DEMATEL technique. To demonstrate the availability and effectiveness of GroupRecD, 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 GroupRecD outperforms other comparison approaches.
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