POI popularity prediction via hierarchical fusion of multiple social clues

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

POI popularity prediction via hierarchical fusion of multiple social clues

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

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Publication Type:: Conference Proceeding
Citation:: SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017, pp. 1001 - 1004
Issue Date:: 2017-08-07

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Field	Value	Language
dc.contributor.author	Duan, Y	en_US
dc.contributor.author	Wang, X	en_US
dc.contributor.author	Yang, Y	en_US
dc.contributor.author	Huang, Z	en_US
dc.contributor.author	Xie, N	en_US
dc.contributor.author	Shen, HT	en_US
dc.date.issued	2017-08-07	en_US
dc.identifier.citation	SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017, pp. 1001 - 1004	en_US
dc.identifier.isbn	9781450350228	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127557
dc.description.abstract	© 2017 Copyright held by the owner/author(s). Predicting the popularity of Point of Interest (POI) has be-come increasingly crucial for location-based services, such as POI recommendation. Most of the existing methods can seldom achieve satisfactory performance due to the scarci-Ty of POI's information, which tendentiously confines the recommendation to popular scenic 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, we devise an effective hierarchy to comprehensively describe POI by integrating various types of media information (e.g., image and text) from multiple social sources. For each individual POI, we simultaneously inject se-mantic knowledge as well as multi-clue representative power. We collect a multi-source POI dataset from four widely-used tourism platforms. Extensive experimental results show that the proposed method can significantly improve the perfor-mance of predicting the attractions' popularity as compared to several baselines.	en_US
dc.relation.ispartof	SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval	en_US
dc.relation.isbasedon	10.1145/3077136.3080701	en_US
dc.title	POI popularity prediction via hierarchical fusion of multiple social clues	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	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.publication-status	Published	en_US

Abstract:

© 2017 Copyright held by the owner/author(s). Predicting the popularity of Point of Interest (POI) has be-come increasingly crucial for location-based services, such as POI recommendation. Most of the existing methods can seldom achieve satisfactory performance due to the scarci-Ty of POI's information, which tendentiously confines the recommendation to popular scenic 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, we devise an effective hierarchy to comprehensively describe POI by integrating various types of media information (e.g., image and text) from multiple social sources. For each individual POI, we simultaneously inject se-mantic knowledge as well as multi-clue representative power. We collect a multi-source POI dataset from four widely-used tourism platforms. Extensive experimental results show that the proposed method can significantly improve the perfor-mance of predicting the attractions' popularity as compared to several baselines.

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http://hdl.handle.net/10453/127557