The most significant advantage of the group recommender system over personalization is the low computational cost because the former analyzes the preferences of many users at once by integrating their preferences. The clustering step is the most time-consuming part of the entire process in a group recommender system. Existing studies either measured the similarities among all users or utilized a clustering algorithm based on the item preference vector to form the groups. However, these existing clustering methods overlooked the clustering cost, and the time complexity was not significantly better than that for personalized recommendations. Therefore, we propose a group recommender system based on the genre preferences of users to dramatically reduce the clustering cost. First, we define a genre preference vector and cluster the groups using this vector. Our group recommender system can reduce the time complexity more efficiently because the number of genres is significantly smaller than the number of items. In addition, we propose a new item preference along with genre weight to subdivide the preferences of users. The evaluation results show that the genre-based group recommender system significantly improves the time efficiency in terms of clustering. Clustering time was about five times faster when using k-means. In addition, for the Gaussian mixture model (GMM), it was about fifty times faster in MovieLens 100 k and about five hundred times faster in Last.fm. The normalized discounted cumulative gain (NDCG) (i.e., accuracy) is not much different from that of the item-based existing studies and is even higher when the number of users is low in a group in MovieLens 100 k.

与个性化相比，群组推荐系统最显着的优势是计算成本低，因为前者通过整合他们的偏好来一次分析许多用户的偏好。在群组推荐系统中，聚类步骤是整个过程中最耗时的部分。现有的研究要么测量所有用户之间的相似性，要么利用基于项目偏好向量的聚类算法来形成组。然而，这些现有的聚类方法忽略了聚类成本，时间复杂度并不明显优于个性化推荐。因此，我们提出了一种基于用户类型偏好的群组推荐系统，以显着降低聚类成本。第一的，我们定义了一个流派偏好向量并使用这个向量对组进行聚类。我们的群组推荐系统可以更有效地降低时间复杂度，因为类型的数量明显小于项目的数量。此外，我们提出了一个新的项目偏好以及流派权重来细分用户的偏好。评估结果表明，基于流派的群组推荐系统在聚类方面显着提高了时间效率。使用 k-means 时，聚类时间大约快五倍。此外，对于高斯混合模型 (GMM)，它在 MovieLens 100 k 中快了大约 50 倍，在 Last.fm 中快了大约 500 倍。归一化贴现累积收益 (NDCG)（即