A collaborative filtering recommendation method based on discrete quantum-inspired shuffled frog leaping algorithms in social networks
In social network recommendation systems, the rating score prediction accuracy of the collaborative filtering (CF) method depends on both the extraction of the nearest neighbors and the calculation of user/project similarity. Based on a similar principle to user/project behavior, this paper uses the...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Wenjuan [verfasserIn] Cao, Jian [verfasserIn] Wu, Jiyi [verfasserIn] Huang, Changqin [verfasserIn] Buyya, Rajkumar [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
Collaborative filtering recommendation |
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| Übergeordnetes Werk: |
Enthalten in: Future generation computer systems - Amsterdam [u.a.] : Elsevier Science, 1984, 88, Seite 262-270 |
|---|---|
| Übergeordnetes Werk: |
volume:88 ; pages:262-270 |
| DOI / URN: |
10.1016/j.future.2018.05.070 |
|---|
| Katalog-ID: |
ELV009783563 |
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| 245 | 1 | 0 | |a A collaborative filtering recommendation method based on discrete quantum-inspired shuffled frog leaping algorithms in social networks |
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| 520 | |a In social network recommendation systems, the rating score prediction accuracy of the collaborative filtering (CF) method depends on both the extraction of the nearest neighbors and the calculation of user/project similarity. Based on a similar principle to user/project behavior, this paper uses the maximum intersection method to extract the optimal neighbor candidate set, and presents a weighted adjusted cosine similarity method to compute user/project similarity. Furthermore, to optimize the weights of the method, a novel optimization method called the discrete quantum-inspired shuffled frog leaping (DQSFL) algorithm is proposed, which is based on the shuffled frog leaping algorithm and quantum information theory. The DQSFL algorithm uses quantum movement equations to search for the optimal location according to the co-evolution of the quantum frog colony. The experiments demonstrate that the CF recommendation method based on DQSFL can effectively solve the rating data sparseness problem in the similarity computation process to improve the accuracy of the rating score prediction, and provide a better recommended result than traditional CF algorithms. | ||
| 650 | 4 | |a Social network | |
| 650 | 4 | |a Collaborative filtering recommendation | |
| 650 | 4 | |a Shuffled frog leaping algorithm | |
| 650 | 4 | |a Quantum information theory | |
| 650 | 4 | |a Rating score prediction accuracy | |
| 700 | 1 | |a Cao, Jian |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Jiyi |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Changqin |e verfasserin |4 aut | |
| 700 | 1 | |a Buyya, Rajkumar |e verfasserin |4 aut | |
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10.1016/j.future.2018.05.070 doi (DE-627)ELV009783563 (ELSEVIER)S0167-739X(18)30023-2 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Li, Wenjuan verfasserin (orcid)0000-0002-3833-2794 aut A collaborative filtering recommendation method based on discrete quantum-inspired shuffled frog leaping algorithms in social networks 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In social network recommendation systems, the rating score prediction accuracy of the collaborative filtering (CF) method depends on both the extraction of the nearest neighbors and the calculation of user/project similarity. Based on a similar principle to user/project behavior, this paper uses the maximum intersection method to extract the optimal neighbor candidate set, and presents a weighted adjusted cosine similarity method to compute user/project similarity. Furthermore, to optimize the weights of the method, a novel optimization method called the discrete quantum-inspired shuffled frog leaping (DQSFL) algorithm is proposed, which is based on the shuffled frog leaping algorithm and quantum information theory. The DQSFL algorithm uses quantum movement equations to search for the optimal location according to the co-evolution of the quantum frog colony. The experiments demonstrate that the CF recommendation method based on DQSFL can effectively solve the rating data sparseness problem in the similarity computation process to improve the accuracy of the rating score prediction, and provide a better recommended result than traditional CF algorithms. Social network Collaborative filtering recommendation Shuffled frog leaping algorithm Quantum information theory Rating score prediction accuracy Cao, Jian verfasserin aut Wu, Jiyi verfasserin aut Huang, Changqin verfasserin aut Buyya, Rajkumar verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 88, Seite 262-270 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:88 pages:262-270 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 88 262-270 |
| spelling |
10.1016/j.future.2018.05.070 doi (DE-627)ELV009783563 (ELSEVIER)S0167-739X(18)30023-2 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Li, Wenjuan verfasserin (orcid)0000-0002-3833-2794 aut A collaborative filtering recommendation method based on discrete quantum-inspired shuffled frog leaping algorithms in social networks 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In social network recommendation systems, the rating score prediction accuracy of the collaborative filtering (CF) method depends on both the extraction of the nearest neighbors and the calculation of user/project similarity. Based on a similar principle to user/project behavior, this paper uses the maximum intersection method to extract the optimal neighbor candidate set, and presents a weighted adjusted cosine similarity method to compute user/project similarity. Furthermore, to optimize the weights of the method, a novel optimization method called the discrete quantum-inspired shuffled frog leaping (DQSFL) algorithm is proposed, which is based on the shuffled frog leaping algorithm and quantum information theory. The DQSFL algorithm uses quantum movement equations to search for the optimal location according to the co-evolution of the quantum frog colony. The experiments demonstrate that the CF recommendation method based on DQSFL can effectively solve the rating data sparseness problem in the similarity computation process to improve the accuracy of the rating score prediction, and provide a better recommended result than traditional CF algorithms. Social network Collaborative filtering recommendation Shuffled frog leaping algorithm Quantum information theory Rating score prediction accuracy Cao, Jian verfasserin aut Wu, Jiyi verfasserin aut Huang, Changqin verfasserin aut Buyya, Rajkumar verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 88, Seite 262-270 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:88 pages:262-270 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 88 262-270 |
| allfields_unstemmed |
10.1016/j.future.2018.05.070 doi (DE-627)ELV009783563 (ELSEVIER)S0167-739X(18)30023-2 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Li, Wenjuan verfasserin (orcid)0000-0002-3833-2794 aut A collaborative filtering recommendation method based on discrete quantum-inspired shuffled frog leaping algorithms in social networks 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In social network recommendation systems, the rating score prediction accuracy of the collaborative filtering (CF) method depends on both the extraction of the nearest neighbors and the calculation of user/project similarity. Based on a similar principle to user/project behavior, this paper uses the maximum intersection method to extract the optimal neighbor candidate set, and presents a weighted adjusted cosine similarity method to compute user/project similarity. Furthermore, to optimize the weights of the method, a novel optimization method called the discrete quantum-inspired shuffled frog leaping (DQSFL) algorithm is proposed, which is based on the shuffled frog leaping algorithm and quantum information theory. The DQSFL algorithm uses quantum movement equations to search for the optimal location according to the co-evolution of the quantum frog colony. The experiments demonstrate that the CF recommendation method based on DQSFL can effectively solve the rating data sparseness problem in the similarity computation process to improve the accuracy of the rating score prediction, and provide a better recommended result than traditional CF algorithms. Social network Collaborative filtering recommendation Shuffled frog leaping algorithm Quantum information theory Rating score prediction accuracy Cao, Jian verfasserin aut Wu, Jiyi verfasserin aut Huang, Changqin verfasserin aut Buyya, Rajkumar verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 88, Seite 262-270 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:88 pages:262-270 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 88 262-270 |
| allfieldsGer |
10.1016/j.future.2018.05.070 doi (DE-627)ELV009783563 (ELSEVIER)S0167-739X(18)30023-2 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Li, Wenjuan verfasserin (orcid)0000-0002-3833-2794 aut A collaborative filtering recommendation method based on discrete quantum-inspired shuffled frog leaping algorithms in social networks 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In social network recommendation systems, the rating score prediction accuracy of the collaborative filtering (CF) method depends on both the extraction of the nearest neighbors and the calculation of user/project similarity. Based on a similar principle to user/project behavior, this paper uses the maximum intersection method to extract the optimal neighbor candidate set, and presents a weighted adjusted cosine similarity method to compute user/project similarity. Furthermore, to optimize the weights of the method, a novel optimization method called the discrete quantum-inspired shuffled frog leaping (DQSFL) algorithm is proposed, which is based on the shuffled frog leaping algorithm and quantum information theory. The DQSFL algorithm uses quantum movement equations to search for the optimal location according to the co-evolution of the quantum frog colony. The experiments demonstrate that the CF recommendation method based on DQSFL can effectively solve the rating data sparseness problem in the similarity computation process to improve the accuracy of the rating score prediction, and provide a better recommended result than traditional CF algorithms. Social network Collaborative filtering recommendation Shuffled frog leaping algorithm Quantum information theory Rating score prediction accuracy Cao, Jian verfasserin aut Wu, Jiyi verfasserin aut Huang, Changqin verfasserin aut Buyya, Rajkumar verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 88, Seite 262-270 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:88 pages:262-270 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 88 262-270 |
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10.1016/j.future.2018.05.070 doi (DE-627)ELV009783563 (ELSEVIER)S0167-739X(18)30023-2 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Li, Wenjuan verfasserin (orcid)0000-0002-3833-2794 aut A collaborative filtering recommendation method based on discrete quantum-inspired shuffled frog leaping algorithms in social networks 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In social network recommendation systems, the rating score prediction accuracy of the collaborative filtering (CF) method depends on both the extraction of the nearest neighbors and the calculation of user/project similarity. Based on a similar principle to user/project behavior, this paper uses the maximum intersection method to extract the optimal neighbor candidate set, and presents a weighted adjusted cosine similarity method to compute user/project similarity. Furthermore, to optimize the weights of the method, a novel optimization method called the discrete quantum-inspired shuffled frog leaping (DQSFL) algorithm is proposed, which is based on the shuffled frog leaping algorithm and quantum information theory. The DQSFL algorithm uses quantum movement equations to search for the optimal location according to the co-evolution of the quantum frog colony. The experiments demonstrate that the CF recommendation method based on DQSFL can effectively solve the rating data sparseness problem in the similarity computation process to improve the accuracy of the rating score prediction, and provide a better recommended result than traditional CF algorithms. Social network Collaborative filtering recommendation Shuffled frog leaping algorithm Quantum information theory Rating score prediction accuracy Cao, Jian verfasserin aut Wu, Jiyi verfasserin aut Huang, Changqin verfasserin aut Buyya, Rajkumar verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 88, Seite 262-270 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:88 pages:262-270 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 88 262-270 |
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004 VZ 54.00 bkl A collaborative filtering recommendation method based on discrete quantum-inspired shuffled frog leaping algorithms in social networks Social network Collaborative filtering recommendation Shuffled frog leaping algorithm Quantum information theory Rating score prediction accuracy |
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ddc 004 bkl 54.00 misc Social network misc Collaborative filtering recommendation misc Shuffled frog leaping algorithm misc Quantum information theory misc Rating score prediction accuracy |
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ddc 004 bkl 54.00 misc Social network misc Collaborative filtering recommendation misc Shuffled frog leaping algorithm misc Quantum information theory misc Rating score prediction accuracy |
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A collaborative filtering recommendation method based on discrete quantum-inspired shuffled frog leaping algorithms in social networks |
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A collaborative filtering recommendation method based on discrete quantum-inspired shuffled frog leaping algorithms in social networks |
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Li, Wenjuan |
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Li, Wenjuan Cao, Jian Wu, Jiyi Huang, Changqin Buyya, Rajkumar |
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a collaborative filtering recommendation method based on discrete quantum-inspired shuffled frog leaping algorithms in social networks |
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A collaborative filtering recommendation method based on discrete quantum-inspired shuffled frog leaping algorithms in social networks |
| abstract |
In social network recommendation systems, the rating score prediction accuracy of the collaborative filtering (CF) method depends on both the extraction of the nearest neighbors and the calculation of user/project similarity. Based on a similar principle to user/project behavior, this paper uses the maximum intersection method to extract the optimal neighbor candidate set, and presents a weighted adjusted cosine similarity method to compute user/project similarity. Furthermore, to optimize the weights of the method, a novel optimization method called the discrete quantum-inspired shuffled frog leaping (DQSFL) algorithm is proposed, which is based on the shuffled frog leaping algorithm and quantum information theory. The DQSFL algorithm uses quantum movement equations to search for the optimal location according to the co-evolution of the quantum frog colony. The experiments demonstrate that the CF recommendation method based on DQSFL can effectively solve the rating data sparseness problem in the similarity computation process to improve the accuracy of the rating score prediction, and provide a better recommended result than traditional CF algorithms. |
| abstractGer |
In social network recommendation systems, the rating score prediction accuracy of the collaborative filtering (CF) method depends on both the extraction of the nearest neighbors and the calculation of user/project similarity. Based on a similar principle to user/project behavior, this paper uses the maximum intersection method to extract the optimal neighbor candidate set, and presents a weighted adjusted cosine similarity method to compute user/project similarity. Furthermore, to optimize the weights of the method, a novel optimization method called the discrete quantum-inspired shuffled frog leaping (DQSFL) algorithm is proposed, which is based on the shuffled frog leaping algorithm and quantum information theory. The DQSFL algorithm uses quantum movement equations to search for the optimal location according to the co-evolution of the quantum frog colony. The experiments demonstrate that the CF recommendation method based on DQSFL can effectively solve the rating data sparseness problem in the similarity computation process to improve the accuracy of the rating score prediction, and provide a better recommended result than traditional CF algorithms. |
| abstract_unstemmed |
In social network recommendation systems, the rating score prediction accuracy of the collaborative filtering (CF) method depends on both the extraction of the nearest neighbors and the calculation of user/project similarity. Based on a similar principle to user/project behavior, this paper uses the maximum intersection method to extract the optimal neighbor candidate set, and presents a weighted adjusted cosine similarity method to compute user/project similarity. Furthermore, to optimize the weights of the method, a novel optimization method called the discrete quantum-inspired shuffled frog leaping (DQSFL) algorithm is proposed, which is based on the shuffled frog leaping algorithm and quantum information theory. The DQSFL algorithm uses quantum movement equations to search for the optimal location according to the co-evolution of the quantum frog colony. The experiments demonstrate that the CF recommendation method based on DQSFL can effectively solve the rating data sparseness problem in the similarity computation process to improve the accuracy of the rating score prediction, and provide a better recommended result than traditional CF algorithms. |
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A collaborative filtering recommendation method based on discrete quantum-inspired shuffled frog leaping algorithms in social networks |
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