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Meta-learning enhanced next POI recommendation by leveraging check-ins from auxiliary cities

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posted on 22.11.2022, 23:55 authored by Jinze Wang

Next point-of-interest (POI) recommendation has attracted a considerate amount of attention in the area of research recently to recommend the next POI where users are most likely to visit at the next time step. However, most existing next POI recommendation algorithms suffer from severe data sparsity issues, due to the scarcity of historical check-in data. Existing studies mainly resort to side information, such as POI categories, to mitigate the data sparsity problem, but ignores the rich check-in information from other cities. To this end, we explore how knowledge transfer from data-rich cities with diverse user patterns can help improve the next POI recommendation performance for cities with sparse check-ins. Accordingly, we propose a novel Meta-learning Enhanced next POI Recommendation (MERec) framework by leveraging check-in data from auxiliary cities, which incorporates the correlation of check-in behaviors among cities into the meta-learning paradigm. Concretely, the MERec framework takes into account the user check-in patterns of the target and auxiliary cities in terms of culture, urban structure, resident behaviour, etc., and transfers more relevant knowledge from more correlated cities. Extensive experiments on four real-world datasets demonstrate the superiority of our proposed MERec framework. 

History

Table of Contents

1 Introduction -- 2 Related work -- 3 Data collection and analysis -- 4 The MERec framework -- 5 Experiments and results -- 6 Conclusion -- References

Notes

A thesis submitted to Macquarie University for the degree of Masters of Research

Awarding Institution

Macquarie University

Degree Type

Thesis MRes

Degree

Thesis (MRes), Macquarie University, Faculty of Science and Engineering, Department of Computing, 2022

Department, Centre or School

Department of Computing

Year of Award

2022

Principal Supervisor

Zhu Sun

Additional Supervisor 1

Yan Wang

Rights

Copyright: The Author Copyright disclaimer: https://www.mq.edu.au/copyright-disclaimer

Language

English

Extent

59 pages