User community preference in Location-Based Social Networks (LBSNs) can meet the diversified location demands of group LBSN users. Although individual's location-based service recommendation or personal spatial preference query problem has been well addressed by many studies, user group or user community preference query is still under way and most only consider the spatial distance factor, which causes accuracy cannot satisfy user demands. To solve the user community spatial preference problem and improve its performance, a knowledge graph-based spatial-aware user community preference query algorithm, Type R-tree (tR-tree) Query Algorithm (TRQA) is proposed to effectively discover user's community preference from LBSNs considering both location semantic information and preference weight of users' Points of Interest (POIs). To achieve this goal, this paper first leverages the tR-tree spatial index to improve query efficiency. Then a community satisfaction degree model based on knowledge graphs is introduced to comprehensively evaluate whether the POI can best meet the preference requirements of a user community. The experimental results show that TRQA has outperformed Perceptual Quality Adaptation Algorithm (PQA) in terms of pruning efficiency and query time. The query time of our proposed algorithm is 80% shorter than PQA as the number of users in the user community changes.

基于位置的社交网络（LBSN）中的用户社区偏好可以满足LBSN组用户的多样化位置需求。尽管许多研究已经很好地解决了个人的基于位置的服务推荐或个人空间偏好查询问题，但是用户组或用户社区偏好查询仍在进行中，并且大多数仅考虑空间距离因素，这导致准确性无法满足用户需求。为了解决用户社区空间偏好问题并提高其性能，提出了一种基于知识图的空间感知用户社区偏好查询算法，即R型树（tR树）查询算法（TRQA），可以有效地发现用户的社区偏好。 LBSN同时考虑位置语义信息和用户兴趣点（POI）的偏好权重。为了实现这一目标，本文首先利用tR树空间索引来提高查询效率。然后引入基于知识图的社区满意度模型，对POI是否能够最好地满足用户社区的偏好进行综合评估。实验结果表明，在修剪效率和查询时间方面，TRQA的性能优于感知质量自适应算法（PQA）。随着用户社区中用户数量的变化，我们提出的算法的查询时间比PQA短80％。实验结果表明，在修剪效率和查询时间方面，TRQA的性能优于感知质量自适应算法（PQA）。随着用户社区中用户数量的变化，我们提出的算法的查询时间比PQA短80％。实验结果表明，在修剪效率和查询时间方面，TRQA的性能优于感知质量自适应算法（PQA）。随着用户社区中用户数量的变化，我们提出的算法的查询时间比PQA短80％。