基于位置社交网络的兴趣点组合推荐算法研究

doi:10.3969/j.issn.1671-1122.2023.09.007

摘要/Abstract

摘要：

随着智能手机的普及和基于用户地理位置信息服务的增多，用户数据量呈爆发式增长，海量数据之间的稀疏性成为了限制基于位置社交网络（Location-Based Social Network，LBSN）的推荐系统性能的一个主要因素。基于此，文章提出了一个基于位置社交网络的兴趣点组合推荐模型（Geographical LightGCN，GLGCN），该模型由协作偏好模块和地理偏好模块两部分组成，其中，协作偏好模块使用图卷积网络深度挖掘用户和兴趣点的嵌入表示，获取用户的协作偏好；地理偏好模块结合兴趣点的相关性和用户轨迹，使用基于门控循环单元（Gate Recurrent Unit，GRU）的序列模型捕获用户的序列偏好。文章将两个模块的推荐分数以线性加权的方式进行组合，得到最终推荐结果。实验表明，相较于现有其他算法，文章提出的组合推荐算法具有更优秀的性能。

关键词: 位置社交网络, 组合推荐, 协作偏好模块, 地理偏好模块

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

中图分类号:

TP309

武伟, 徐莎莎, 郭森森, 李晓宇. 基于位置社交网络的兴趣点组合推荐算法研究[J]. 信息网络安全, 2023, 23(9): 75-84.

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.

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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%