Knowledge Graph-Based Spatial-Aware User Community Preference Query Algorithm for LBSNs

Abstract

Abstract 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.