Research on Hybrid Recommendation Algorithm for Points of Interest in Location-Based Social Network

doi:10.3969/j.issn.1671-1122.2023.09.007

Abstract

Abstract:

With the popularization of smartphones and the increasing use of services utilizing geolocation information, user data has experienced explosive growth, and the sparsity of massive data has become a major factor limiting the performance of recommendation systems in location-based social network (LBSN). Regarding this, this paper proposed a LBSN point-of-interest hybrid recommendation algorithm named Geographical LightGCN (GLGCN), which consists of a collaborative preference module and a geographical preference module. The collaborative preference module utilized the graph convolutional network to deeply mine the embedded representations of users and their interest points, thereby obtaining users’ collaborative preferences. Meanwhile, the geographical preference module combined the relevance of interest points and user trajectories, capturing users' sequence preferences with a sequence model based on the gate recurrent unit (GRU). The final recommendation results were obtained by combining the recommendation scores of the two modules in a linearly weighted manner. The experiments indicate that the hybrid recommendation algorithm proposed in this paper exhibits superior recommendation performance compared to other existing algorithms.

Key words: location-based social network, hybrid recommendation, collaborative preference module, geographical preference module

CLC Number:

TP309

WU Wei, XU Shasha, GUO Sensen, LI Xiaoyu. Research on Hybrid Recommendation Algorithm for Points of Interest in Location-Based Social Network[J]. Netinfo Security, 2023, 23(9): 75-84.

Figures/Tables 8

References 32

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数据集	用户数	项目数	交互数	稀疏度
Gowalla	29858	40981	1027370	99%
Foursquare	1083	42981	227428	99%