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Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks
Joint Authors
Liu, Xiyu
Guo, Lei
Jiang, Haoran
Xing, Changming
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-04
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
As one of the important techniques to explore unknown places for users, the methods that are proposed for point-of-interest (POI) recommendation have been widely studied in recent years.
Compared with traditional recommendation problems, POI recommendations are suffering from more challenges, such as the cold-start and one-class collaborative filtering problems.
Many existing studies have focused on how to overcome these challenges by exploiting different types of contexts (e.g., social and geographical information).
However, most of these methods only model these contexts as regularization terms, and the deep information hidden in the network structure has not been fully exploited.
On the other hand, neural network-based embedding methods have shown its power in many recommendation tasks with its ability to extract high-level representations from raw data.
According to the above observations, to well utilize the network information, a neural network-based embedding method (node2vec) is first exploited to learn the user and POI representations from a social network and a predefined location network, respectively.
To deal with the implicit feedback, a pair-wise ranking-based method is then introduced.
Finally, by regarding the pretrained network representations as the priors of the latent feature factors, an embedding-based POI recommendation method is proposed.
As this method consists of an embedding model and a collaborative filtering model, when the training data are absent, the predictions will mainly be generated by the extracted embeddings.
In other cases, this method will learn the user and POI factors from these two components.
Experiments on two real-world datasets demonstrate the importance of the network embeddings and the effectiveness of our proposed method.
American Psychological Association (APA)
Guo, Lei& Jiang, Haoran& Liu, Xiyu& Xing, Changming. 2019. Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks. Complexity،Vol. 2019, no. 2019, pp.1-18.
https://search.emarefa.net/detail/BIM-1131455
Modern Language Association (MLA)
Guo, Lei…[et al.]. Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks. Complexity No. 2019 (2019), pp.1-18.
https://search.emarefa.net/detail/BIM-1131455
American Medical Association (AMA)
Guo, Lei& Jiang, Haoran& Liu, Xiyu& Xing, Changming. Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks. Complexity. 2019. Vol. 2019, no. 2019, pp.1-18.
https://search.emarefa.net/detail/BIM-1131455
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1131455