Interpretable recommendation via attraction modeling: Learning multilevel attractiveness over multimodal movie contents

Hu, L; Jian, S; Cao, L; Chen, Q

Interpretable recommendation via attraction modeling: Learning multilevel attractiveness over multimodal movie contents

Hu, L

Jian, S Cao, L

Chen, Q

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Publication Type:: Conference Proceeding
Citation:: IJCAI International Joint Conference on Artificial Intelligence, 2018, 2018-July pp. 3400 - 3406
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Hu, L https://orcid.org/0000-0001-8588-2177	en_US
dc.contributor.author	Jian, S	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Chen, Q	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	IJCAI International Joint Conference on Artificial Intelligence, 2018, 2018-July pp. 3400 - 3406	en_US
dc.identifier.isbn	9780999241127	en_US
dc.identifier.issn	1045-0823	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131859
dc.description.abstract	© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. New contents like blogs and online videos are produced in every second in the new media age. We argue that attraction is one of the decisive factors for user selection of new contents. However, collaborative filtering cannot work without user feedback; and the existing content-based recommender systems are ineligible to capture and interpret the attractive points on new contents. Accordingly, we propose attraction modeling to learn and interpret user attractiveness. Specially, we build a multilevel attraction model (MLAM) over the content features-the story (textual data) and cast members (categorical data) of movies. In particular, we design multilevel personal filters to calculate users' attractiveness on words, sentences and cast members at different levels. The experimental results show the superiority of MLAM over the state-of-the-art methods. In addition, a case study is provided to demonstrate the interpretability of MLAM by visualizing user attractiveness on a movie.	en_US
dc.relation.ispartof	IJCAI International Joint Conference on Artificial Intelligence	en_US
dc.title	Interpretable recommendation via attraction modeling: Learning multilevel attractiveness over multimodal movie contents	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-July	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2018-July	en_US

Abstract:

© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. New contents like blogs and online videos are produced in every second in the new media age. We argue that attraction is one of the decisive factors for user selection of new contents. However, collaborative filtering cannot work without user feedback; and the existing content-based recommender systems are ineligible to capture and interpret the attractive points on new contents. Accordingly, we propose attraction modeling to learn and interpret user attractiveness. Specially, we build a multilevel attraction model (MLAM) over the content features-the story (textual data) and cast members (categorical data) of movies. In particular, we design multilevel personal filters to calculate users' attractiveness on words, sentences and cast members at different levels. The experimental results show the superiority of MLAM over the state-of-the-art methods. In addition, a case study is provided to demonstrate the interpretability of MLAM by visualizing user attractiveness on a movie.

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http://hdl.handle.net/10453/131859