基于LBSN和多图融合的兴趣点推荐

doi:10.11947/j.AGCS.2022.20210156

摘要/Abstract

摘要： 兴趣点推荐作为推荐领域的一个重要分支一直备受研究者青睐。本文提出一种基于位置的社交网络(LBSN)和多图融合的兴趣点推荐方法GraphPOI。综合分析用户和兴趣点的内在因素和外部表征，首先，对用户-兴趣点的评分矩阵进行学习得到用户和兴趣点的内部潜在向量；其次，根据评分矩阵构造用户-兴趣点交互图，得到兴趣点在用户空间的表征向量以及用户在兴趣点空间的表征向量；然后，对兴趣点按其地理位置进行聚类，得到兴趣点在位置空间的表征向量，结合兴趣点在用户空间的表征向量进而得到兴趣点的外部表征向量；对用户社交图中的信息扩散现象进行建模，捕获用户的朋友关系，得到用户在社交空间的表征向量，结合用户在兴趣点空间的表征向量进而得到用户的外部表征向量；最后，结合用户和兴趣点的内部潜在向量与外部表征向量，得到用户和兴趣点的最终向量表示，并将其输入到多层神经网络模型中进行评分预测。在Yelp数据集上对所提模型进行验证，结果表明本文方法能够有效提升兴趣点推荐的准确性。

Abstract: As an important branch of the recommendation field, point of interest (POI) recommendation has always been favored by researchers. This paper proposes a POI recommendation algorithm based on location-based social network (LBSN) and multi-graph fusion, GraphPOI. It comprehensively analyzes the internal factors and external representations of users and POIs. First, it learns from the user-POI rating matrix to obtain the internal latent vector of users and POIs. Then, it constructs a user-POI interaction diagram according to the rating matrix, and obtains the representation vector of the POI in the user space and the representation vector of the user in the POI space. Next,it clusters the POIs according to their geographic locations to obtain the representation vector of the POI in the location space, and combines the representation vector of the POI in the user space to obtain the POI's external representation vector. At the same time, it models the information diffusion phenomenon in the user's social graph, captures the user's friendship to obtain the user's representation vector in the social space, and combines the user's representation vector in the POI space to obtain the user's external representation vector. Last, the internal latent vector and external representation vector of the user and the POI are combined to obtain the final vector representation of the user and the POI, which is input into the multi-layer neural network model for scoring prediction. The proposed model is verified on the Yelp dataset, and the results demonstrate that the method proposed in this paper can effectively improve the accuracy of POI recommendation.

Key words: POI recommendation, location-based social network, clustering, multi-graph fusion, vector representation

中图分类号:

P208

方金凤, 孟祥福. 基于LBSN和多图融合的兴趣点推荐[J]. 测绘学报, 2022, 51(5): 739-749.

FANG Jinfeng, MENG Xiangfu. POI recommendation based on LBSN and multi-graph fusion[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(5): 739-749.

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