Personalized QoS Prediction for Service Recommendation With a Service-Oriented Tensor Model
Quality of Service (QoS) value is usually unknown in service recommendation practice. There are some matrix factorization approaches for predicting the unknown value with a user-service model, which uses a single collaboration with the user's neighbor when looking for different services. Howeve...
Saved in:
| Published in | IEEE access Vol. 7; pp. 55721 - 55731 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Quality of Service (QoS) value is usually unknown in service recommendation practice. There are some matrix factorization approaches for predicting the unknown value with a user-service model, which uses a single collaboration with the user's neighbor when looking for different services. However, the QoS value is highly related to the service provider and participants. The services are considered in various collaboration based on different users. By considering the context of services, this paper proposes a QoS prediction model using tensor decomposition based on service collaboration called Service-oriented Tensor (SOT). The prediction approach analyzes service collaboration from other similar services and relevant users by using a three-order tensor. Compared with the traditional model, the experiment results show that the proposed model achieves better prediction accuracy. |
|---|---|
| AbstractList | Quality of Service (QoS) value is usually unknown in service recommendation practice. There are some matrix factorization approaches for predicting the unknown value with a user-service model, which uses a single collaboration with the user's neighbor when looking for different services. However, the QoS value is highly related to the service provider and participants. The services are considered in various collaboration based on different users. By considering the context of services, this paper proposes a QoS prediction model using tensor decomposition based on service collaboration called Service-oriented Tensor (SOT). The prediction approach analyzes service collaboration from other similar services and relevant users by using a three-order tensor. Compared with the traditional model, the experiment results show that the proposed model achieves better prediction accuracy. |
| Author | Guo, Lantian Cai, Xiaoyan Tian, Gang Hao, Fei Mu, Dejun |
| Author_xml | – sequence: 1 givenname: Lantian orcidid: 0000-0002-1792-4926 surname: Guo fullname: Guo, Lantian organization: School of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, China – sequence: 2 givenname: Dejun orcidid: 0000-0002-2568-0861 surname: Mu fullname: Mu, Dejun organization: School of Automation, Northwestern Polytechnical University, Xi'an, China – sequence: 3 givenname: Xiaoyan orcidid: 0000-0002-1406-107X surname: Cai fullname: Cai, Xiaoyan organization: School of Automation, Northwestern Polytechnical University, Xi'an, China – sequence: 4 givenname: Gang orcidid: 0000-0003-0161-3020 surname: Tian fullname: Tian, Gang organization: School of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China – sequence: 5 givenname: Fei orcidid: 0000-0001-5288-5523 surname: Hao fullname: Hao, Fei email: fhao@snnu.edu.cn organization: School of Computer Science, Shaanxi Normal University, Xi'an, China |
| BookMark | eNpNUctOwzAQtFCRKIUv6CUS5xS_Yx-ripdUxKMgDhwsx9mAqzYGOyDB15M2ULGXXc3szEo7h2jQhAYQGhM8IQTr0-lsdrZYTCgmekI1oQKLPTSkROqcCSYH_-YDdJzSEnelOkgUQ_R8CzGFxq78N1TZXVhktxEq71ofmqwOMVtA_PQOsntwYb2GprJb6sm3r5n9Y_Ob6KFpO4cHaFKnug4VrI7Qfm1XCY5_-wg9np89zC7z-c3F1Ww6zx3Hqs1FYZ1wSnGumaIU12XBqJaMFKrWjumyxkTiAhOwssJcMKgFOIV1UZbSgWUjdNX7VsEuzVv0axu_TLDebIEQX4yNrXcrMKLSsgKsalYAV6UrmS4IBltSq6TrDo_QSe_1FsP7B6TWLMNH7P6TDOVCSCIk590W67dcDClFqHdXCTabUEwfitmEYn5D6VTjXuUBYKdQUnOtFPsB16mJfg |
| CODEN | IAECCG |
| CitedBy_id | crossref_primary_10_1007_s10489_021_02851_z crossref_primary_10_1109_ACCESS_2019_2937879 crossref_primary_10_3934_mbe_2022068 crossref_primary_10_1109_TSC_2020_2995571 crossref_primary_10_3390_info12060242 crossref_primary_10_1016_j_jmsy_2020_12_019 crossref_primary_10_1109_ACCESS_2020_3043773 crossref_primary_10_1016_j_future_2020_03_062 crossref_primary_10_1109_TSC_2020_3041626 |
| Cites_doi | 10.1016/j.neucom.2019.01.028 10.1016/j.eswa.2018.11.003 10.1109/ICWS.2011.86 10.1109/ICWS.2014.51 10.1109/TSC.2016.2584058 10.1109/ACCESS.2016.2573314 10.1109/TKDE.2018.2840993 10.1007/978-3-319-90092-6_10 10.1137/07070111X 10.1186/s13673-018-0161-6 10.1007/978-3-642-41230-1_20 10.1186/s13673-017-0094-5 10.1145/2843948 10.1109/ICWS.2009.30 10.1109/TSMCA.2012.2210409 10.1016/j.engappai.2014.10.010 10.1109/TSC.2012.31 10.1109/ICWS.2007.140 10.1109/TPAMI.2014.2353639 10.1007/s10586-016-0565-x 10.1007/s10619-017-7199-8 10.1109/TSC.2011.59 10.1145/2566486.2568001 10.1109/SCC.2012.36 10.1145/371920.372071 10.1155/2019/8950508 10.1109/TCYB.2014.2374695 10.1109/TSC.2010.52 10.1109/ISSRE.2011.17 10.1109/TETC.2013.2274044 10.1109/ICWS.2014.32 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 |
| DBID | 97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D DOA |
| DOI | 10.1109/ACCESS.2019.2912505 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library Online CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts METADEX Technology Research Database Materials Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Materials Research Database Engineered Materials Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace METADEX Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Materials Research Database |
| Database_xml | – sequence: 1 dbid: DOA name: Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE Electronic Library Online url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2169-3536 |
| EndPage | 55731 |
| ExternalDocumentID | oai_doaj_org_article_5d96de08f37e48bcb39710eab2a86cb7 10_1109_ACCESS_2019_2912505 8694988 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 61672433; 61872296; 61772429; 61702317; 61702305 funderid: 10.13039/501100001809 |
| GroupedDBID | 0R~ 4.4 5VS 6IK 97E AAJGR ABVLG ACGFS ADBBV ALMA_UNASSIGNED_HOLDINGS BCNDV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD ESBDL GROUPED_DOAJ IFIPE IPLJI JAVBF KQ8 M43 M~E O9- OCL OK1 RIA RIE RIG RNS AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c408t-57ac5c8844938220fb732963178f9c39bf0160701ea6d0453ef5ec8097bb6cea3 |
| IEDL.DBID | DOA |
| ISSN | 2169-3536 |
| IngestDate | Tue Oct 22 15:08:16 EDT 2024 Thu Oct 10 15:50:09 EDT 2024 Fri Aug 23 00:50:48 EDT 2024 Wed Jun 26 19:28:06 EDT 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c408t-57ac5c8844938220fb732963178f9c39bf0160701ea6d0453ef5ec8097bb6cea3 |
| ORCID | 0000-0002-1406-107X 0000-0003-0161-3020 0000-0002-2568-0861 0000-0002-1792-4926 0000-0001-5288-5523 |
| OpenAccessLink | https://doaj.org/article/5d96de08f37e48bcb39710eab2a86cb7 |
| PQID | 2455615644 |
| PQPubID | 4845423 |
| PageCount | 11 |
| ParticipantIDs | ieee_primary_8694988 proquest_journals_2455615644 crossref_primary_10_1109_ACCESS_2019_2912505 doaj_primary_oai_doaj_org_article_5d96de08f37e48bcb39710eab2a86cb7 |
| PublicationCentury | 2000 |
| PublicationDate | 20190000 2019-00-00 20190101 2019-01-01 |
| PublicationDateYYYYMMDD | 2019-01-01 |
| PublicationDate_xml | – year: 2019 text: 20190000 |
| PublicationDecade | 2010 |
| PublicationPlace | Piscataway |
| PublicationPlace_xml | – name: Piscataway |
| PublicationTitle | IEEE access |
| PublicationTitleAbbrev | Access |
| PublicationYear | 2019 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref34 ref12 ref15 ref14 tang (ref18) 2012 ref31 ref30 ref33 ref32 ref10 ref2 ref1 ref17 ref16 ref19 ref24 mnih (ref35) 2007 ref23 ref26 ref25 ref20 ref22 ref21 kuleshov (ref28) 2015 ref29 ref8 kluver (ref11) 2018 ref7 ref9 ref4 ref3 cichocki (ref27) 2009 ref6 ref5 paterek (ref36) 2007 |
| References_xml | – ident: ref5 doi: 10.1016/j.neucom.2019.01.028 – ident: ref12 doi: 10.1016/j.eswa.2018.11.003 – start-page: 5 year: 2007 ident: ref36 article-title: Improving regularized singular value decomposition for collaborative filtering publication-title: Proc KDD Cup Workshop contributor: fullname: paterek – ident: ref15 doi: 10.1109/ICWS.2011.86 – ident: ref17 doi: 10.1109/ICWS.2014.51 – ident: ref25 doi: 10.1109/TSC.2016.2584058 – start-page: 202 year: 2012 ident: ref18 article-title: Location-aware collaborative filtering for QoS-based service recommendation publication-title: Proc of the IEEE International Conf on Web services (ICWS) contributor: fullname: tang – ident: ref1 doi: 10.1109/ACCESS.2016.2573314 – ident: ref6 doi: 10.1109/TKDE.2018.2840993 – start-page: 344 year: 2018 ident: ref11 article-title: Rating-based collaborative filtering: Algorithms and evaluation publication-title: Social Information Access doi: 10.1007/978-3-319-90092-6_10 contributor: fullname: kluver – ident: ref29 doi: 10.1137/07070111X – ident: ref4 doi: 10.1186/s13673-018-0161-6 – year: 2009 ident: ref27 publication-title: Nonnegative Matrix and Tensor Factorizations Applications to Exploratory Multi-Way Data Analysis and Blind Source Separation contributor: fullname: cichocki – ident: ref23 doi: 10.1007/978-3-642-41230-1_20 – ident: ref9 doi: 10.1186/s13673-017-0094-5 – start-page: 1257 year: 2007 ident: ref35 article-title: Probabilistic matrix factorization publication-title: Proc 21st Int Conf Neural Inf Process Syst (NIPS) contributor: fullname: mnih – ident: ref8 doi: 10.1145/2843948 – start-page: 507 year: 2015 ident: ref28 article-title: Tensor factorization via matrix factorization publication-title: Artificial Intelligence and Statistics contributor: fullname: kuleshov – ident: ref34 doi: 10.1109/ICWS.2009.30 – ident: ref16 doi: 10.1109/TSMCA.2012.2210409 – ident: ref22 doi: 10.1016/j.engappai.2014.10.010 – ident: ref14 doi: 10.1109/TSC.2012.31 – ident: ref32 doi: 10.1109/ICWS.2007.140 – ident: ref19 doi: 10.1109/TPAMI.2014.2353639 – ident: ref10 doi: 10.1007/s10586-016-0565-x – ident: ref3 doi: 10.1007/s10619-017-7199-8 – ident: ref21 doi: 10.1109/TSC.2011.59 – ident: ref30 doi: 10.1145/2566486.2568001 – ident: ref20 doi: 10.1109/SCC.2012.36 – ident: ref33 doi: 10.1145/371920.372071 – ident: ref2 doi: 10.1155/2019/8950508 – ident: ref31 doi: 10.1109/TCYB.2014.2374695 – ident: ref13 doi: 10.1109/TSC.2010.52 – ident: ref26 doi: 10.1109/ISSRE.2011.17 – ident: ref7 doi: 10.1109/TETC.2013.2274044 – ident: ref24 doi: 10.1109/ICWS.2014.32 |
| SSID | ssj0000816957 |
| Score | 2.2569773 |
| Snippet | Quality of Service (QoS) value is usually unknown in service recommendation practice. There are some matrix factorization approaches for predicting the unknown... |
| SourceID | doaj proquest crossref ieee |
| SourceType | Open Website Aggregation Database Publisher |
| StartPage | 55721 |
| SubjectTerms | Collaboration Computational modeling Data models Mathematical analysis Model accuracy Prediction models Predictive models QoS prediction Quality of service service collaboration service recommendation Service-oriented tensor Solid modeling tensor decomposition Tensors |
| SummonAdditionalLinks | – databaseName: IEEE Electronic Library Online dbid: RIE link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1NT9wwEB0Bp_bQD6DqtrTygSNZnNiO7SNdFSEkWipARerBip2ximh3q232wq-vJ3FWlPbALUrixMmzPc_2zBuAfUw2HoMQBba-KqRvVNGUxhYieBM1tsHWFCh89qk-uZKn1-p6Aw7WsTCI2Duf4ZQO-738dhFWtFR2aGorrTGbsGl4NcRqrddTKIGEVToLC5XcHh7NZukbyHvLTitbkq3_y_j0Gv05qco_I3FvXo6fw9lYscGr5Ha66vw03D3QbHxszV_As8wz2dHQMF7CBs634ek99cEd-HY-EvE7bNmXxQU7X9K2DUHFEpdleRxhNEX9mV4wpF9iX2-676wZrxafSSk58VZ2mWbEqRRlV_uxC1fHHy9nJ0XOtVAEyU1XKN0EFYyR0orEGXj0WlSpc5baRBuE9bGXouMlNnWbaKDAqDAYbrX3dcBGvIKt-WKOr4FpH1EkjHkZhORemzZGFYUMJOxTRT6BgxEE92uQ1HD9VIRbN2DmCDOXMZvABwJqfSvpYfcn0g92uXs51dq6RW6i0CiNDz7RrJJj46vG1MHrCewQKOuHZDwmsDfC7nLf_e0qSSlDVSKKb_5f6i08oQoOCzF7sNUtV_guUZPOv-_b5B8YdOIH priority: 102 providerName: IEEE |
| Title | Personalized QoS Prediction for Service Recommendation With a Service-Oriented Tensor Model |
| URI | https://ieeexplore.ieee.org/document/8694988 https://www.proquest.com/docview/2455615644 https://doaj.org/article/5d96de08f37e48bcb39710eab2a86cb7 |
| Volume | 7 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV09T8MwELVQJxgQUBCFUnlgJGDHdmyPpaKqkIAiikBisGLnLJCgoFIWfj124qIiBhbWfPtdfPcu8b1D6BBCjAfHWAaVzTNuS5GVVOmMOau8hMrpIhYKX1wWo1t-fi_ul1p9xTVhjTxwA9yJqHRRAVGeSeDKOhsCKCVQ2rxUhbNNHTkVS8lU7YMVLbSQSWaIEn3SHwzCiOJaLn2caxoj_49QVCv2pxYrv_xyHWyGG2g9sUTcb55uE63AdAutLWkHttHDeEGjP6HC1683eDyLP10i0DgwUZy8AI4J5ssLpOZJ-O5p_ojLxd7sKuocB9aJJyGfDWfF3mjP2-h2eDYZjLLUKSFznKh5JmTphFOKc81CxCfeSpaHqUWl8toxbX0tJEcolEUVSBwDL8ApoqW1hYOS7aDW9HUKuwhL64EFCxHqGCdWqsp74Rl3UZYn96SDjhagmbdGEMPUiQTRpsHYRIxNwriDTiOw34dGNet6Q7CxSTY2f9m4g9rRLN8XUYXmWqkO6i7MZNLMezc5jw0_RaB5e_9x6320GofTfHTpotZ89gEHgYbMba9-43p1xeAXwXjaPw |
| link.rule.ids | 315,786,790,802,870,2115,4043,27954,27955,27956,55107 |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwEB6VcgAOvErF9gE-cGy2TmzH9rFdUS3QLUVsRSUOVuyMBQJ2UZu99NfjyWNFgQO3KIkTJ5_t-WzPfAPwCpONxyBEhrUvMukrlVW5sZkI3kSNdbAlBQrPzsrphXx7qS434GAdC4OIrfMZjumw3cuvl2FFS2WHprTSGnMH7iY7z3UXrbVeUaEUElbpXloo5_bwaDJJX0H-W3Zc2Jys_S3z06r092lV_hqLWwNz8ghmQ9U6v5Jv41Xjx-HmD9XG_637Y3jYM0121DWNJ7CBi6fw4Df9wS34fD5Q8Rus2YflR3Z-RRs3BBZLbJb1IwmjSeqP9IIuARP79LX5wqrhavaetJITc2XzNCdOpSi_2vdncHHyej6ZZn22hSxIbppM6SqoYIyUViTWwKPXokjdM9cm2iCsj60YHc-xKutEBAVGhcFwq70vA1ZiGzYXywU-B6Z9RJFQ5nkQkntt6hhVFDKQtE8R-QgOBhDcz05Uw7WTEW5dh5kjzFyP2QiOCaj1raSI3Z5IP9j1Hcyp2pY1chOFRml88Ilo5RwrX1SmDF6PYItAWT-kx2MEewPsru-9166QlDRUJaq48-9SL-HedD47dadvzt7twn2qbLcsswebzdUK9xNRafyLtn3-Aiad5Vs |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Personalized+QoS+Prediction+for+Service+Recommendation+With+a+Service-Oriented+Tensor+Model&rft.jtitle=IEEE+access&rft.au=Guo%2C+Lantian&rft.au=Mu%2C+Dejun&rft.au=Cai%2C+Xiaoyan&rft.au=Tian%2C+Gang&rft.date=2019&rft.issn=2169-3536&rft.eissn=2169-3536&rft.volume=7&rft.spage=55721&rft.epage=55731&rft_id=info:doi/10.1109%2FACCESS.2019.2912505&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_ACCESS_2019_2912505 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2169-3536&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2169-3536&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2169-3536&client=summon |