Hybrid Learning Model with Barzilai-Borwein Optimization for Context-aware Recommendations

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3 Citationer (Scopus)
16 Downloads (Pure)

Resumé

We propose an improved learning model for non-negative matrix factorization in the context-aware recommendation. We extend the collective non-negative matrix factorization through hybrid regularization method by combining multiplicative update rules with Barzilai-Borwein optimization. This provides new improved way of learning factorized matrices. We combine ratings, content features, and contextual information in three different 2-dimensional matrices. We study the performance of the proposed method on recommending top-N items. The method was empirically tested on 4 datasets, including movies, music, and mobile apps, showing an improvement in comparison with other state-of-the-art for top-N recommendations, and time convergence to the stationary point for larger datasets.
OriginalsprogEngelsk
TitelProceedings of the Thirtieth-First Florida Artificial Intelligence Research Society Conference
Antal sider6
ForlagAAAI Publications
Publikationsdato21 maj 2018
Sider456-461
StatusUdgivet - 21 maj 2018
BegivenhedInternational Florida Artificial Intelligence Research Society Conference - Crowne Plaza Melbourne Oceanfront, Melbourne, USA
Varighed: 21 maj 201823 maj 2018
Konferencens nummer: 31
https://sites.google.com/site/flairs31conference/

Konference

KonferenceInternational Florida Artificial Intelligence Research Society Conference
Nummer31
LokationCrowne Plaza Melbourne Oceanfront
LandUSA
ByMelbourne
Periode21/05/201823/05/2018
Internetadresse

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Factorization
Computer music
Application programs

Citer dette

Da Costa, F. S., & Dolog, P. (2018). Hybrid Learning Model with Barzilai-Borwein Optimization for Context-aware Recommendations. I Proceedings of the Thirtieth-First Florida Artificial Intelligence Research Society Conference (s. 456-461). AAAI Publications.
Da Costa, Felipe Soares ; Dolog, Peter. / Hybrid Learning Model with Barzilai-Borwein Optimization for Context-aware Recommendations. Proceedings of the Thirtieth-First Florida Artificial Intelligence Research Society Conference. AAAI Publications, 2018. s. 456-461
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title = "Hybrid Learning Model with Barzilai-Borwein Optimization for Context-aware Recommendations",
abstract = "We propose an improved learning model for non-negative matrix factorization in the context-aware recommendation. We extend the collective non-negative matrix factorization through hybrid regularization method by combining multiplicative update rules with Barzilai-Borwein optimization. This provides new improved way of learning factorized matrices. We combine ratings, content features, and contextual information in three different 2-dimensional matrices. We study the performance of the proposed method on recommending top-N items. The method was empirically tested on 4 datasets, including movies, music, and mobile apps, showing an improvement in comparison with other state-of-the-art for top-N recommendations, and time convergence to the stationary point for larger datasets.",
author = "{Da Costa}, {Felipe Soares} and Peter Dolog",
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Da Costa, FS & Dolog, P 2018, Hybrid Learning Model with Barzilai-Borwein Optimization for Context-aware Recommendations. i Proceedings of the Thirtieth-First Florida Artificial Intelligence Research Society Conference. AAAI Publications, s. 456-461, Melbourne, USA, 21/05/2018.

Hybrid Learning Model with Barzilai-Borwein Optimization for Context-aware Recommendations. / Da Costa, Felipe Soares; Dolog, Peter.

Proceedings of the Thirtieth-First Florida Artificial Intelligence Research Society Conference. AAAI Publications, 2018. s. 456-461.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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AB - We propose an improved learning model for non-negative matrix factorization in the context-aware recommendation. We extend the collective non-negative matrix factorization through hybrid regularization method by combining multiplicative update rules with Barzilai-Borwein optimization. This provides new improved way of learning factorized matrices. We combine ratings, content features, and contextual information in three different 2-dimensional matrices. We study the performance of the proposed method on recommending top-N items. The method was empirically tested on 4 datasets, including movies, music, and mobile apps, showing an improvement in comparison with other state-of-the-art for top-N recommendations, and time convergence to the stationary point for larger datasets.

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Da Costa FS, Dolog P. Hybrid Learning Model with Barzilai-Borwein Optimization for Context-aware Recommendations. I Proceedings of the Thirtieth-First Florida Artificial Intelligence Research Society Conference. AAAI Publications. 2018. s. 456-461