Hierarchical Multi-Clue Modelling for POI Popularity Prediction with Heterogeneous Tourist Information

Yang, Y; Duan, Y; Wang, X; Huang, Z; Xie, N; Shen, HT

Hierarchical Multi-Clue Modelling for POI Popularity Prediction with Heterogeneous Tourist Information

Yang, Y Duan, Y Wang, X Huang, Z Xie, N Shen, HT

Permalink

Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2019, 31 (4), pp. 757 - 768
Issue Date:: 2019-04-01

Closed Access

	Filename	Description	Size
	08370064.pdf	Published Version	1.74 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Yang, Y	en_US
dc.contributor.author	Duan, Y	en_US
dc.contributor.author	Wang, X	en_US
dc.contributor.author	Huang, Z	en_US
dc.contributor.author	Xie, N	en_US
dc.contributor.author	Shen, HT https://orcid.org/0000-0002-2999-2088	en_US
dc.date.issued	2019-04-01	en_US
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2019, 31 (4), pp. 757 - 768	en_US
dc.identifier.issn	1041-4347	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132014
dc.description.abstract	© 1989-2012 IEEE. Predicting the popularity of Point of Interest (POI) has become increasingly crucial for location-based services, such as POI recommendation. Most of the existing methods can seldom achieve satisfactory performance due to the scarcity of POI's information, which tendentiously confines the recommendation to popular scene spots, and ignores the unpopular attractions with potentially precious values. In this paper, we propose a novel approach, termed Hierarchical Multi-Clue Fusion (HMCF), for predicting the popularity of POIs. Specifically, in order to cope with the problem of data sparsity, we propose to comprehensively describe POI using various types of user generated content (UGC) (e.g., text and image) from multiple sources. Then, we devise an effective POI modelling method in a hierarchical manner, which simultaneously injects semantic knowledge as well as multi-clue representative power into POIs. For evaluation, we construct a multi-source POI dataset by collecting all the textual and visual content of several specific provinces in China from four main-stream tourism platforms during 2006 to 2017. Extensive experimental results show that the proposed method can significantly improve the performance of predicting the attractions' popularity as compared to several baseline methods.	en_US
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering	en_US
dc.relation.isbasedon	10.1109/TKDE.2018.2842190	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Hierarchical Multi-Clue Modelling for POI Popularity Prediction with Heterogeneous Tourist Information	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	31	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	31	en_US

Abstract:

© 1989-2012 IEEE. Predicting the popularity of Point of Interest (POI) has become increasingly crucial for location-based services, such as POI recommendation. Most of the existing methods can seldom achieve satisfactory performance due to the scarcity of POI's information, which tendentiously confines the recommendation to popular scene spots, and ignores the unpopular attractions with potentially precious values. In this paper, we propose a novel approach, termed Hierarchical Multi-Clue Fusion (HMCF), for predicting the popularity of POIs. Specifically, in order to cope with the problem of data sparsity, we propose to comprehensively describe POI using various types of user generated content (UGC) (e.g., text and image) from multiple sources. Then, we devise an effective POI modelling method in a hierarchical manner, which simultaneously injects semantic knowledge as well as multi-clue representative power into POIs. For evaluation, we construct a multi-source POI dataset by collecting all the textual and visual content of several specific provinces in China from four main-stream tourism platforms during 2006 to 2017. Extensive experimental results show that the proposed method can significantly improve the performance of predicting the attractions' popularity as compared to several baseline methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/132014