Aspect-Driven User Preference and News Representation Learning for News Recommendation

Lu, W; Wang, R; Wang, S; Peng, X; Wu, H; Zhang, Q

Aspect-Driven User Preference and News Representation Learning for News Recommendation

Lu, W Wang, R Wang, S

Peng, X

Wu, H Zhang, Q

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Intelligent Transportation Systems, 2022, PP, (99), pp. 1-11
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Lu, W
dc.contributor.author	Wang, R
dc.contributor.author	Wang, S https://orcid.org/0000-0003-1133-9379
dc.contributor.author	Peng, X https://orcid.org/0000-0002-8901-1472
dc.contributor.author	Wu, H
dc.contributor.author	Zhang, Q
dc.date.accessioned	2022-09-06T04:22:59Z
dc.date.available	2022-09-06T04:22:59Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Intelligent Transportation Systems, 2022, PP, (99), pp. 1-11
dc.identifier.issn	1524-9050
dc.identifier.issn	1558-0016
dc.identifier.uri	http://hdl.handle.net/10453/161420
dc.description.abstract	Intelligent human-device interfaces play key roles in fully automated vehicles (FAVs), ensuring smooth interactions and improving the driving experience. Listening to news is a popular method of relaxing during a journey; as a result, travelers require automatic recommendations of preferred news programs. Most existing news recommender systems usually learn topic-level representations of users and news for recommendations while neglecting to learn more informative aspect-level features, resulting in limited recommendation performance. To bridge this significant gap, we propose a novel Aspect-driven News Recommender System (ANRS) built on aspect-level user preferences and news representation learning. In ANRS, a <italic>news aspect-level encoder</italic> and a <italic>user aspect-level encoder</italic> are devised to learn the fine-grained aspect-level representations of users’ preferences and news characteristics respectively. These representations are subsequently fed into a <italic>click predictor</italic> to predict the probability of a given user clicking on the candidate news item. Extensive experiments demonstrate the superiority of our method over state-of-the-art baseline methods.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Intelligent Transportation Systems
dc.relation.isbasedon	10.1109/TITS.2022.3182568
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0905 Civil Engineering, 1507 Transportation and Freight Services
dc.subject.classification	Logistics & Transportation
dc.title	Aspect-Driven User Preference and News Representation Learning for News Recommendation
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0905 Civil Engineering
utslib.for	1507 Transportation and Freight Services
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 - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2022-09-06T04:22:57Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Intelligent human-device interfaces play key roles in fully automated vehicles (FAVs), ensuring smooth interactions and improving the driving experience. Listening to news is a popular method of relaxing during a journey; as a result, travelers require automatic recommendations of preferred news programs. Most existing news recommender systems usually learn topic-level representations of users and news for recommendations while neglecting to learn more informative aspect-level features, resulting in limited recommendation performance. To bridge this significant gap, we propose a novel Aspect-driven News Recommender System (ANRS) built on aspect-level user preferences and news representation learning. In ANRS, a news aspect-level encoder and a user aspect-level encoder are devised to learn the fine-grained aspect-level representations of users’ preferences and news characteristics respectively. These representations are subsequently fed into a click predictor to predict the probability of a given user clicking on the candidate news item. Extensive experiments demonstrate the superiority of our method over state-of-the-art baseline methods.

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http://hdl.handle.net/10453/161420