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] Wang, Gaowei [verfasserIn] Hsu, Ching-Hsien [verfasserIn] Wang, Hongya [verfasserIn] |
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 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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| 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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10.1007/s00500-018-3406-4 doi (DE-627)SPR006499910 (SPR)s00500-018-3406-4-e DE-627 ger DE-627 rakwb eng Xiao, Yingyuan verfasserin aut A time-sensitive personalized recommendation method based on probabilistic matrix factorization technique 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Matrix factorization (dpeaa)DE-He213 Context-dependent similarity measurement (dpeaa)DE-He213 Context (dpeaa)DE-He213 Wang, Gaowei verfasserin aut Hsu, Ching-Hsien verfasserin aut Wang, Hongya verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 22(2018), 20 vom: 07. Aug., Seite 6785-6796 (DE-627)SPR006469531 nnns volume:22 year:2018 number:20 day:07 month:08 pages:6785-6796 https://dx.doi.org/10.1007/s00500-018-3406-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 22 2018 20 07 08 6785-6796 |
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10.1007/s00500-018-3406-4 doi (DE-627)SPR006499910 (SPR)s00500-018-3406-4-e DE-627 ger DE-627 rakwb eng Xiao, Yingyuan verfasserin aut A time-sensitive personalized recommendation method based on probabilistic matrix factorization technique 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Matrix factorization (dpeaa)DE-He213 Context-dependent similarity measurement (dpeaa)DE-He213 Context (dpeaa)DE-He213 Wang, Gaowei verfasserin aut Hsu, Ching-Hsien verfasserin aut Wang, Hongya verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 22(2018), 20 vom: 07. Aug., Seite 6785-6796 (DE-627)SPR006469531 nnns volume:22 year:2018 number:20 day:07 month:08 pages:6785-6796 https://dx.doi.org/10.1007/s00500-018-3406-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 22 2018 20 07 08 6785-6796 |
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10.1007/s00500-018-3406-4 doi (DE-627)SPR006499910 (SPR)s00500-018-3406-4-e DE-627 ger DE-627 rakwb eng Xiao, Yingyuan verfasserin aut A time-sensitive personalized recommendation method based on probabilistic matrix factorization technique 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Matrix factorization (dpeaa)DE-He213 Context-dependent similarity measurement (dpeaa)DE-He213 Context (dpeaa)DE-He213 Wang, Gaowei verfasserin aut Hsu, Ching-Hsien verfasserin aut Wang, Hongya verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 22(2018), 20 vom: 07. Aug., Seite 6785-6796 (DE-627)SPR006469531 nnns volume:22 year:2018 number:20 day:07 month:08 pages:6785-6796 https://dx.doi.org/10.1007/s00500-018-3406-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 22 2018 20 07 08 6785-6796 |
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10.1007/s00500-018-3406-4 doi (DE-627)SPR006499910 (SPR)s00500-018-3406-4-e DE-627 ger DE-627 rakwb eng Xiao, Yingyuan verfasserin aut A time-sensitive personalized recommendation method based on probabilistic matrix factorization technique 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Matrix factorization (dpeaa)DE-He213 Context-dependent similarity measurement (dpeaa)DE-He213 Context (dpeaa)DE-He213 Wang, Gaowei verfasserin aut Hsu, Ching-Hsien verfasserin aut Wang, Hongya verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 22(2018), 20 vom: 07. Aug., Seite 6785-6796 (DE-627)SPR006469531 nnns volume:22 year:2018 number:20 day:07 month:08 pages:6785-6796 https://dx.doi.org/10.1007/s00500-018-3406-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 22 2018 20 07 08 6785-6796 |
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10.1007/s00500-018-3406-4 doi (DE-627)SPR006499910 (SPR)s00500-018-3406-4-e DE-627 ger DE-627 rakwb eng Xiao, Yingyuan verfasserin aut A time-sensitive personalized recommendation method based on probabilistic matrix factorization technique 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Matrix factorization (dpeaa)DE-He213 Context-dependent similarity measurement (dpeaa)DE-He213 Context (dpeaa)DE-He213 Wang, Gaowei verfasserin aut Hsu, Ching-Hsien verfasserin aut Wang, Hongya verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 22(2018), 20 vom: 07. Aug., Seite 6785-6796 (DE-627)SPR006469531 nnns volume:22 year:2018 number:20 day:07 month:08 pages:6785-6796 https://dx.doi.org/10.1007/s00500-018-3406-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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. |
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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. |
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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. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR006499910</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124002850.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-018-3406-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR006499910</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-018-3406-4-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xiao, Yingyuan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A time-sensitive personalized recommendation method based on probabilistic matrix factorization technique</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Personalized recommendation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Matrix factorization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Context-dependent similarity measurement</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Context</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Gaowei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hsu, Ching-Hsien</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Hongya</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">22(2018), 20 vom: 07. Aug., Seite 6785-6796</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:22</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:20</subfield><subfield code="g">day:07</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:6785-6796</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-018-3406-4</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">22</subfield><subfield code="j">2018</subfield><subfield code="e">20</subfield><subfield code="b">07</subfield><subfield code="c">08</subfield><subfield code="h">6785-6796</subfield></datafield></record></collection>
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