FAGRec: Alleviating data sparsity in POI recommendations via the feature-aware graph learning

Xia Liu; Liwan Wu

doi:10.3934/era.2024123

journal article Jan 01, 2024

FAGRec: Alleviating data sparsity in POI recommendations via the feature-aware graph learning

Xia Liu

Liwan Wu

Electronic Research Archive Vol. 32 No. 4 pp. 2728-2744 · American Institute of Mathematical Sciences (AIMS)

View at Publisher Save 10.3934/era.2024123

Abstract

<abstract>

Point-of-interest (POI) recommendation has attracted great attention in the field of recommender systems over the past decade. Various techniques, such as those based on matrix factorization and deep neural networks, have demonstrated outstanding performance. However, these methods are susceptible to the impact of data sparsity. Data sparsity is a significant characteristic of POI recommendation, where some POIs have limited interaction records and, in extreme cases, become cold-start POIs with no interaction history. To alleviate the influence of data sparsity on model performance, this paper introduced FAGRec, a POI-recommendation model based on the feature-aware graph. The key idea was to construct an interaction graph between POIs and their initial features. This allows the transformation of POI features into a weighted aggregation of initial features. Different POIs can share the learned representations of initial features, thereby mitigating the issue of data sparsity. Furthermore, we proposed attention-based graph neural networks and a user preference estimation method based on delayed time factors for learning representations of POIs and users, contributing to the generation of recommendations. Experimental results on two real-world datasets demonstrate the effectiveness of FAGRec in the task of POI recommendation.

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References

Details

Published: Jan 01, 2024
Vol/Issue: 32(4)
Pages: 2728-2744

Authors

X

Xia Liu

School of Physics and Electronic Information, Yantai University, Yantai 264005, China

L

Liwan Wu

Information Technology Center, Guangzhou Health Science College, Guangzhou 510450, China

Cite This Article

Xia Liu, Liwan Wu (2024). FAGRec: Alleviating data sparsity in POI recommendations via the feature-aware graph learning. Electronic Research Archive, 32(4), 2728-2744. https://doi.org/10.3934/era.2024123

FAGRec: Alleviating data sparsity in POI recommendations via the feature-aware graph learning

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