A time-sensitive personalized recommendation method based on probabilistic matrix factorization technique
Abstract Personalized recommender systems are the most effective way to solve the problem of information overload. The majority of traditional personalized recommender systems employ the collaborative filtering (CF) approach. CF leverages users’ behaviors to infer a target user’s preference for a pa...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Xiao, Yingyuan [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 22(2018), 20 vom: 07. Aug., Seite 6785-6796 |
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| Übergeordnetes Werk: |
volume:22 ; year:2018 ; number:20 ; day:07 ; month:08 ; pages:6785-6796 |
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| DOI / URN: |
10.1007/s00500-018-3406-4 |
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OLC2034892976 |
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| 520 | |a Abstract Personalized recommender systems are the most effective way to solve the problem of information overload. The majority of traditional personalized recommender systems employ the collaborative filtering (CF) approach. CF leverages users’ behaviors to infer a target user’s preference for a particular item, while ignores the fact that users interact with the system within a particular context, such as a particular time interval or location. In this paper, we propose a novel time-sensitive personalized recommendation method called TSPR for movie recommendation. Specifically, we first define and construct a new user–context rating matrix based on the original user–movie rating matrix and then propose a novel context-dependent similarity measurement by mining the implicit relationship among users from the user–context rating matrix. Further, we build a context-dependent similarity matrix based on the context-dependent similarity measurement. Finally, we incorporate the context-dependent similarity matrix into the probabilistic matrix factorization model. The experimental results show that TSPR performs much better than the state-of-the-art recommendation methods. | ||
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| 700 | 1 | |a Wang, Hongya |4 aut | |
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10.1007/s00500-018-3406-4 doi (DE-627)OLC2034892976 (DE-He213)s00500-018-3406-4-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Xiao, Yingyuan verfasserin aut A time-sensitive personalized recommendation method based on probabilistic matrix factorization technique 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Personalized recommender systems are the most effective way to solve the problem of information overload. The majority of traditional personalized recommender systems employ the collaborative filtering (CF) approach. CF leverages users’ behaviors to infer a target user’s preference for a particular item, while ignores the fact that users interact with the system within a particular context, such as a particular time interval or location. In this paper, we propose a novel time-sensitive personalized recommendation method called TSPR for movie recommendation. Specifically, we first define and construct a new user–context rating matrix based on the original user–movie rating matrix and then propose a novel context-dependent similarity measurement by mining the implicit relationship among users from the user–context rating matrix. Further, we build a context-dependent similarity matrix based on the context-dependent similarity measurement. Finally, we incorporate the context-dependent similarity matrix into the probabilistic matrix factorization model. The experimental results show that TSPR performs much better than the state-of-the-art recommendation methods. Personalized recommendation Matrix factorization Context-dependent similarity measurement Context Wang, Gaowei aut Hsu, Ching-Hsien aut Wang, Hongya aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 22(2018), 20 vom: 07. Aug., Seite 6785-6796 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:22 year:2018 number:20 day:07 month:08 pages:6785-6796 https://doi.org/10.1007/s00500-018-3406-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 22 2018 20 07 08 6785-6796 |
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Abstract Personalized recommender systems are the most effective way to solve the problem of information overload. The majority of traditional personalized recommender systems employ the collaborative filtering (CF) approach. CF leverages users’ behaviors to infer a target user’s preference for a particular item, while ignores the fact that users interact with the system within a particular context, such as a particular time interval or location. In this paper, we propose a novel time-sensitive personalized recommendation method called TSPR for movie recommendation. Specifically, we first define and construct a new user–context rating matrix based on the original user–movie rating matrix and then propose a novel context-dependent similarity measurement by mining the implicit relationship among users from the user–context rating matrix. Further, we build a context-dependent similarity matrix based on the context-dependent similarity measurement. Finally, we incorporate the context-dependent similarity matrix into the probabilistic matrix factorization model. The experimental results show that TSPR performs much better than the state-of-the-art recommendation methods. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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Abstract Personalized recommender systems are the most effective way to solve the problem of information overload. The majority of traditional personalized recommender systems employ the collaborative filtering (CF) approach. CF leverages users’ behaviors to infer a target user’s preference for a particular item, while ignores the fact that users interact with the system within a particular context, such as a particular time interval or location. In this paper, we propose a novel time-sensitive personalized recommendation method called TSPR for movie recommendation. Specifically, we first define and construct a new user–context rating matrix based on the original user–movie rating matrix and then propose a novel context-dependent similarity measurement by mining the implicit relationship among users from the user–context rating matrix. Further, we build a context-dependent similarity matrix based on the context-dependent similarity measurement. Finally, we incorporate the context-dependent similarity matrix into the probabilistic matrix factorization model. The experimental results show that TSPR performs much better than the state-of-the-art recommendation methods. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Personalized recommender systems are the most effective way to solve the problem of information overload. The majority of traditional personalized recommender systems employ the collaborative filtering (CF) approach. CF leverages users’ behaviors to infer a target user’s preference for a particular item, while ignores the fact that users interact with the system within a particular context, such as a particular time interval or location. In this paper, we propose a novel time-sensitive personalized recommendation method called TSPR for movie recommendation. Specifically, we first define and construct a new user–context rating matrix based on the original user–movie rating matrix and then propose a novel context-dependent similarity measurement by mining the implicit relationship among users from the user–context rating matrix. Further, we build a context-dependent similarity matrix based on the context-dependent similarity measurement. Finally, we incorporate the context-dependent similarity matrix into the probabilistic matrix factorization model. The experimental results show that TSPR performs much better than the state-of-the-art recommendation methods. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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A time-sensitive personalized recommendation method based on probabilistic matrix factorization technique |
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https://doi.org/10.1007/s00500-018-3406-4 |
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Wang, Gaowei Hsu, Ching-Hsien Wang, Hongya |
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Wang, Gaowei Hsu, Ching-Hsien Wang, Hongya |
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10.1007/s00500-018-3406-4 |
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