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GeoMF++: Scalable Location Recommendation via Joint Geographical Modeling and Matrix Factorization

Authors:

Xing XieAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 36, Issue 3

Article No.: 33, Pages 1 - 29

https://doi.org/10.1145/3182166

Published: 23 March 2018 Publication History

Abstract

Location recommendation is an important means to help people discover attractive locations. However, extreme sparsity of user-location matrices leads to a severe challenge, so it is necessary to take implicit feedback characteristics of user mobility data into account and leverage the location’s spatial information. To this end, based on previously developed GeoMF, we propose a scalable and flexible framework, dubbed GeoMF++, for joint geographical modeling and implicit feedback-based matrix factorization. We then develop an efficient optimization algorithm for parameter learning, which scales linearly with data size and the total number of neighbor grids of all locations. GeoMF++ can be well explained from two perspectives. First, it subsumes two-dimensional kernel density estimation so that it captures spatial clustering phenomenon in user mobility data; Second, it is strongly connected with widely used neighbor additive models, graph Laplacian regularized models, and collective matrix factorization. Finally, we extensively evaluate GeoMF++ on two large-scale LBSN datasets. The experimental results show that GeoMF++ consistently outperforms the state-of-the-art and other competing baselines on both datasets in terms of NDCG and Recall. Besides, the efficiency studies show that GeoMF++ is much more scalable with the increase of data size and the dimension of latent space.

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Index Terms

GeoMF++: Scalable Location Recommendation via Joint Geographical Modeling and Matrix Factorization
1. Information systems

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Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 36, Issue 3

July 2018

402 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3146384

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

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Copyright © 2018 ACM.

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Association for Computing Machinery

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Publication History

Published: 23 March 2018

Accepted: 01 January 2018

Revised: 01 December 2017

Received: 01 August 2017

Published in TOIS Volume 36, Issue 3

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https://doi.org/10.1109/TSC.2023.3342812
Zheng XNi ZZhong XLuo Y(2024)Kernelized Deep Learning for Matrix Factorization Recommendation System Using Explicit and Implicit InformationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318294235:1(1205-1216)Online publication date: Jan-2024
https://doi.org/10.1109/TNNLS.2022.3182942
Lian DGao ZSong XLi YLiu QChen E(2024)Training Recommenders Over Large Item Corpus With Importance SamplingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.334465736:12(9433-9447)Online publication date: 1-Dec-2024
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