A Hybrid Probabilistic Multiobjective Evolutionary Algorithm for Commercial Recommendation Systems

As big-data-driven complex systems, commercial recommendation systems (RSs) have been widely used in such companies as Amazon and Ebay. Their core aim is to maximize total profit, which relies on recommendation accuracy and profits from recommended items. It is also important for them to treat new i...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transactions on computational social systems Ročník 8; číslo 3; s. 589 - 598
Hlavní autori:	Wei, Guoshuai, Wu, Quanwang, Zhou, Mengchu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.06.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accuracy Cold start Complex systems Evolutionary algorithms Evolutionary computation Genetic algorithms Genetics Linear programming Measurement multiobjective evolutionary algorithm (MOEA) Optimization Pareto optimization Probabilistic logic profit recommendation system (RS) Recommender systems
ISSN:	2329-924X, 2373-7476
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	As big-data-driven complex systems, commercial recommendation systems (RSs) have been widely used in such companies as Amazon and Ebay. Their core aim is to maximize total profit, which relies on recommendation accuracy and profits from recommended items. It is also important for them to treat new items equally for a long-term run. However, traditional recommendation techniques mainly focus on recommendation accuracy and suffer from a cold-start problem (i.e., new items cannot be recommended). Differing from them, this work designs a multiobjective RS by considering item profit and novelty besides accuracy. Then, a hybrid probabilistic multiobjective evolutionary algorithm (MOEA) is proposed to optimize these conflicting metrics. In it, some specifically designed genetic operators are proposed, and two classical MOEA frameworks are adaptively combined such that it owns their complementary advantages. The experimental results reveal that it outperforms some state-of-the-art algorithms as it achieves a higher hypervolume value than them.
AbstractList	As big-data-driven complex systems, commercial recommendation systems (RSs) have been widely used in such companies as Amazon and Ebay. Their core aim is to maximize total profit, which relies on recommendation accuracy and profits from recommended items. It is also important for them to treat new items equally for a long-term run. However, traditional recommendation techniques mainly focus on recommendation accuracy and suffer from a cold-start problem (i.e., new items cannot be recommended). Differing from them, this work designs a multiobjective RS by considering item profit and novelty besides accuracy. Then, a hybrid probabilistic multiobjective evolutionary algorithm (MOEA) is proposed to optimize these conflicting metrics. In it, some specifically designed genetic operators are proposed, and two classical MOEA frameworks are adaptively combined such that it owns their complementary advantages. The experimental results reveal that it outperforms some state-of-the-art algorithms as it achieves a higher hypervolume value than them.
Author	Wu, Quanwang Zhou, Mengchu Wei, Guoshuai
Author_xml	– sequence: 1 givenname: Guoshuai surname: Wei fullname: Wei, Guoshuai email: wgs0208@foxmail.com organization: College of Computer Science, Chongqing University, Chongqing, China – sequence: 2 givenname: Quanwang orcidid: 0000-0001-8155-6200 surname: Wu fullname: Wu, Quanwang email: wqw@cqu.edu.cn organization: College of Computer Science, Chongqing University, Chongqing, China – sequence: 3 givenname: Mengchu orcidid: 0000-0002-5408-8752 surname: Zhou fullname: Zhou, Mengchu email: zhou@njit.edu organization: Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USA
BookMark	eNp9kF9LwzAUxYNMcM59APEl4HNnmtu0zeMo0wkTxU3wraRpqhltM5N0sG9v64YPPvh0_3DOvYffJRq1plUIXYdkFoaE322y9XpGCQ1nQBhLKZyhMYUEgiRK4tHQUx5wGr1foKlzW0JISBlLKBmjYo6Xh8LqEr9YU4hC19p5LfFTV3ttiq2SXu8VXuxN3fWLVtgDntcfxmr_2eDKWJyZplFWalHjVyWHoS3FIMXrg_OqcVfovBK1U9NTnaC3-8UmWwar54fHbL4KJOXgg4iWEUSKRTIWjFRlmZCSF33WlAgheAlEUiiqinER0VQBKBELDnGailJBv5ig2-PdnTVfnXI-35rOtv3LnDKAiJOQkV6VHFXSGuesqnKp_U9eb4Wu85DkA9N8YJoPTPMT094Z_nHurG56IP96bo4erZT61fepASiFb9n2hag
CODEN	ITCSGL
CitedBy_id	crossref_primary_10_1016_j_procs_2023_01_114 crossref_primary_10_1109_TCE_2023_3318754 crossref_primary_10_1007_s10489_023_05105_2 crossref_primary_10_1109_TEVC_2022_3166815 crossref_primary_10_1109_TCSS_2022_3140310 crossref_primary_10_1007_s10489_023_05212_0 crossref_primary_10_1016_j_engappai_2024_108477 crossref_primary_10_1109_TETCI_2022_3230942 crossref_primary_10_1109_TNSE_2021_3079415 crossref_primary_10_3390_s22010011 crossref_primary_10_3389_fonc_2025_1604041 crossref_primary_10_1016_j_ins_2024_120921 crossref_primary_10_1109_TCYB_2021_3126711 crossref_primary_10_1016_j_jksuci_2024_102237 crossref_primary_10_1109_TCSS_2022_3158318 crossref_primary_10_1109_TCYB_2021_3079346 crossref_primary_10_3390_math10101739 crossref_primary_10_1109_TETCI_2024_3359093 crossref_primary_10_1109_TETCI_2022_3189084 crossref_primary_10_1109_TASE_2023_3296733 crossref_primary_10_1177_18724981251346889 crossref_primary_10_1016_j_swevo_2024_101548 crossref_primary_10_1109_TCSS_2023_3266520 crossref_primary_10_1109_JIOT_2023_3262849 crossref_primary_10_1007_s10489_024_05854_8 crossref_primary_10_1109_TKDE_2022_3222047 crossref_primary_10_1016_j_ins_2022_03_068 crossref_primary_10_1177_14727978251376402 crossref_primary_10_3389_fpubh_2025_1588270 crossref_primary_10_1109_TBDATA_2021_3090905 crossref_primary_10_1007_s40747_025_01891_z crossref_primary_10_1109_TCSS_2022_3195816 crossref_primary_10_1016_j_eswa_2022_119457 crossref_primary_10_1007_s10489_025_06781_y crossref_primary_10_1109_TCYB_2021_3119591 crossref_primary_10_1109_JAS_2024_124806 crossref_primary_10_1109_JAS_2021_1004308 crossref_primary_10_1007_s10844_025_00935_7 crossref_primary_10_1109_TCYB_2022_3185554 crossref_primary_10_1142_S0219467825500433 crossref_primary_10_1109_TETCI_2022_3186673 crossref_primary_10_1109_TITS_2022_3140351 crossref_primary_10_1109_TEVC_2021_3091615 crossref_primary_10_1016_j_eswa_2025_126636 crossref_primary_10_1109_TASE_2023_3267609 crossref_primary_10_1145_3674982 crossref_primary_10_1016_j_apenergy_2023_121123 crossref_primary_10_1007_s11042_023_15141_2 crossref_primary_10_1109_TNNLS_2021_3105901 crossref_primary_10_1007_s42979_024_02667_x
Cites_doi	10.1016/j.jpdc.2016.10.014 10.1007/978-3-030-23381-5_4 10.1109/MC.2009.263 10.1109/ACCESS.2020.2990193 10.1016/j.ins.2019.11.028 10.1016/j.eswa.2017.08.008 10.1109/TASE.2018.2862380 10.1109/JSYST.2018.2883214 10.1109/TSMC.2015.2507161 10.1016/j.eswa.2017.06.020 10.1109/TASE.2019.2918691 10.1109/TITS.2016.2638898 10.1109/JAS.2020.1003177 10.1109/MCI.2014.2369894 10.1016/j.physa.2016.02.021 10.1109/TCSS.2019.2932288 10.1109/TNNLS.2019.2955567 10.1016/j.knosys.2016.04.018 10.1145/3298689.3346998 10.1109/TII.2019.2958696 10.1109/TCBB.2017.2705094 10.1109/TCYB.2016.2545688 10.1109/TCSS.2019.2938239 10.1109/JAS.2017.7510538 10.1109/TKDE.2019.2895033 10.1109/JAS.2020.1003204 10.1145/963770.963772 10.1016/j.knosys.2013.03.012 10.1109/4235.996017 10.1016/j.eswa.2020.113648 10.1007/s11280-017-0437-1 10.1109/TEVC.2007.892759 10.1109/JAS.2019.1911450 10.1007/3-540-36970-8_2 10.1103/PhysRevE.76.046115 10.1109/TEVC.2013.2281535 10.1016/j.ejor.2006.08.008 10.1109/CEC.2016.7743898 10.1109/TSMC.2018.2818175 10.1109/TBDATA.2019.2916868 10.1109/TII.2015.2443723 10.1155/2018/1716352 10.1145/3376916
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021
DBID	97E RIA RIE AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D
DOI	10.1109/TCSS.2021.3055823
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional
DatabaseTitleList	Computer and Information Systems Abstracts
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Social Sciences (General)
EISSN	2373-7476
EndPage	598
ExternalDocumentID	10_1109_TCSS_2021_3055823 9363322
Genre	orig-research
GrantInformation_xml	– fundername: National Natural Science Foundation of China grantid: 61702060; 61672117 funderid: 10.13039/501100001809 – fundername: Deanship of Scientific Research (DSR) at King Abdulaziz University grantid: D-503-135-1441 funderid: 10.13039/501100004054
GroupedDBID	0R~ 4.4 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD IFIPE IPLJI JAVBF M43 OCL PQQKQ RIA RIE AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c293t-42d434e54c6a50fdd70d9b00080aaa9d30c23bff59a428e33ea6a93688ade38e3
IEDL.DBID	RIE
ISICitedReferencesCount	75
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000655822700006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2329-924X
IngestDate	Sun Nov 30 04:11:53 EST 2025 Sat Nov 29 01:37:08 EST 2025 Tue Nov 18 22:30:58 EST 2025 Wed Aug 27 02:30:23 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	3
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c293t-42d434e54c6a50fdd70d9b00080aaa9d30c23bff59a428e33ea6a93688ade38e3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-8155-6200 0000-0002-5408-8752
PQID	2533490150
PQPubID	2040411
PageCount	10
ParticipantIDs	proquest_journals_2533490150 crossref_citationtrail_10_1109_TCSS_2021_3055823 crossref_primary_10_1109_TCSS_2021_3055823 ieee_primary_9363322
PublicationCentury	2000
PublicationDate	2021-06-01
PublicationDateYYYYMMDD	2021-06-01
PublicationDate_xml	– month: 06 year: 2021 text: 2021-06-01 day: 01
PublicationDecade	2020
PublicationPlace	Piscataway
PublicationPlace_xml	– name: Piscataway
PublicationTitle	IEEE transactions on computational social systems
PublicationTitleAbbrev	TCSS
PublicationYear	2021
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	ref35 ref13 ref34 ref12 ref37 ref15 ref36 ref14 ref31 ref30 ref33 ref11 ref10 ref2 ref1 ref39 ref17 ref38 ref16 ref19 ref18 milojkovic (ref22) 2019 zitzler (ref26) 2001; 103 ref46 ref24 ref45 ref23 guo (ref32) 2020 ref25 ref20 ref42 ref41 ref44 ref21 ref43 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40
References_xml	– ident: ref12 doi: 10.1016/j.jpdc.2016.10.014 – ident: ref5 doi: 10.1007/978-3-030-23381-5_4 – ident: ref33 doi: 10.1109/MC.2009.263 – ident: ref29 doi: 10.1109/ACCESS.2020.2990193 – ident: ref23 doi: 10.1016/j.ins.2019.11.028 – ident: ref2 doi: 10.1016/j.eswa.2017.08.008 – ident: ref46 doi: 10.1109/TASE.2018.2862380 – ident: ref41 doi: 10.1109/JSYST.2018.2883214 – ident: ref31 doi: 10.1109/TSMC.2015.2507161 – ident: ref3 doi: 10.1016/j.eswa.2017.06.020 – ident: ref16 doi: 10.1109/TASE.2019.2918691 – ident: ref19 doi: 10.1109/TITS.2016.2638898 – ident: ref44 doi: 10.1109/JAS.2020.1003177 – ident: ref11 doi: 10.1109/MCI.2014.2369894 – ident: ref8 doi: 10.1016/j.physa.2016.02.021 – ident: ref42 doi: 10.1109/TCSS.2019.2932288 – ident: ref43 doi: 10.1109/TNNLS.2019.2955567 – ident: ref13 doi: 10.1016/j.knosys.2016.04.018 – ident: ref18 doi: 10.1145/3298689.3346998 – ident: ref6 doi: 10.1109/TII.2019.2958696 – ident: ref35 doi: 10.1109/TCBB.2017.2705094 – ident: ref37 doi: 10.1109/TCYB.2016.2545688 – ident: ref39 doi: 10.1109/TCSS.2019.2938239 – ident: ref38 doi: 10.1109/JAS.2017.7510538 – ident: ref10 doi: 10.1109/TKDE.2019.2895033 – ident: ref45 doi: 10.1109/JAS.2020.1003204 – ident: ref4 doi: 10.1145/963770.963772 – ident: ref1 doi: 10.1016/j.knosys.2013.03.012 – ident: ref15 doi: 10.1109/4235.996017 – ident: ref24 doi: 10.1016/j.eswa.2020.113648 – ident: ref9 doi: 10.1007/s11280-017-0437-1 – year: 2020 ident: ref32 article-title: Multiresource-constrained selective disassembly with maximal profit and minimal energy consumption publication-title: IEEE Trans Autom Sci Eng – ident: ref30 doi: 10.1109/TEVC.2007.892759 – ident: ref21 doi: 10.1109/JAS.2019.1911450 – ident: ref34 doi: 10.1007/3-540-36970-8_2 – ident: ref7 doi: 10.1103/PhysRevE.76.046115 – ident: ref25 doi: 10.1109/TEVC.2013.2281535 – ident: ref17 doi: 10.1016/j.ejor.2006.08.008 – ident: ref28 doi: 10.1109/CEC.2016.7743898 – ident: ref20 doi: 10.1109/TSMC.2018.2818175 – year: 2019 ident: ref22 article-title: Multi-gradient descent for multi-objective recommender systems publication-title: arXiv 2001 00846 – volume: 103 start-page: 1 year: 2001 ident: ref26 article-title: SPEA2: Improving the strength Pareto evolutionary algorithm publication-title: TIK-Report – ident: ref40 doi: 10.1109/TBDATA.2019.2916868 – ident: ref36 doi: 10.1109/TII.2015.2443723 – ident: ref14 doi: 10.1155/2018/1716352 – ident: ref27 doi: 10.1145/3376916
SSID	ssj0001255720
Score	2.4515755
Snippet	As big-data-driven complex systems, commercial recommendation systems (RSs) have been widely used in such companies as Amazon and Ebay. Their core aim is to...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	589
SubjectTerms	Accuracy Cold start Complex systems Evolutionary algorithms Evolutionary computation Genetic algorithms Genetics Linear programming Measurement multiobjective evolutionary algorithm (MOEA) Optimization Pareto optimization Probabilistic logic profit recommendation system (RS) Recommender systems
Title	A Hybrid Probabilistic Multiobjective Evolutionary Algorithm for Commercial Recommendation Systems
URI	https://ieeexplore.ieee.org/document/9363322 https://www.proquest.com/docview/2533490150
Volume	8
WOSCitedRecordID	wos000655822700006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2373-7476 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001255720 issn: 2329-924X databaseCode: RIE dateStart: 20140101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NT8IwFH8B4sGLX2hE0fTgQY2Dbe2-joRAOBhCAhpuS7t2ikFmxiDhv7ftCtFoTLxtS7uvX9v30fd7D-CGsZBy4qYW5g61iNSwrYikxCIuF2ESSIGioyqfH4PhMJxOo1EFHnZcGCGEDj4TLXWo9_J5lqyUq6wdYR_LAViFahD4JVfriz_F8wJ3u3Hp2FF70h2PpQHoOi2V1Sp08TfRo2up_FiAtVTpH_7vfY7gwGiPqFPCfQwVsTiBRkmxRWaaLtGtySV9VwfWQYONImWhUS5nroqEVYmZkebdZuytXO5Qb21GIM03qDN_yfJZ8fqOpEKLFINEVWWSD1CmqjwxZZiQSXZ-Ck_93qQ7sExZBSuRsr1QMBBMhEcSn3p2ynlg80grAzalNOLYTlzM0tSLqLRNBMaC-lR-aihRFVheOIPaIluIc0BhQKUCZwuSMkpCzJjDhHA4lXaO5_EkaIC9_eNxYnKOq9IX81jbHnYUK5BiBVJsQGrA_a7LR5lw46_GdYXKrqEBpAHNLayxmZLL2FWkY-3gufi91yXsq3uXcWBNqBX5SlzBXrIuZsv8Wo-2T1oU1QY
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bT8IwFD7BS6Iv3tCIovbBBzUOurVj2yMhEIxISEDD29KunWIUzEAT_r1tV4hGY-LbtrTptq_tufR85wCccx4yQb3UIcJlDlUathPRlDrUEzJMAiVQTFTlQyfodsPhMOoV4HrJhZFSmuAzWdGX5ixfTJJ37SqrRqRG1ARcgTWfUg_nbK0vHhXfD7zF0aWLo-qg0e8rE9BzKzqvVeiRb8LHVFP5sQUbudLa_t8b7cCW1R9RPQd8FwpyvAelnGSL7EKdogubTfqyCLyO2nNNy0K9TK1dHQurUzMjw7yd8Od8w0PNDzsHWTZH9ZfHSTaaPb0ipdIizSHRdZnUANpYVTe2EBOy6c734b7VHDTaji2s4CRKus80EJRQ6dOkxnycChFgERl1ADPGIkFw4hGepn7ElHUiCZGsxtSnhgpXSdSDA1gdT8byEFAYMKXCYUlTzmhIOHe5lK5gytLxfZEEJcCLPx4nNuu4Ln7xEhvrA0exBinWIMUWpBJcLbu85Sk3_mpc1KgsG1pASlBewBrbRTmNPU07Ni6eo997ncFGe3DXiTs33dtj2NTj5FFhZVidZe_yBNaTj9lomp2amfcJkunYTQ
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=A+Hybrid+Probabilistic+Multiobjective+Evolutionary+Algorithm+for+Commercial+Recommendation+Systems&rft.jtitle=IEEE+transactions+on+computational+social+systems&rft.au=Guoshuai+Wei&rft.au=Wu%2C+Quanwang&rft.au=Zhou%2C+Mengchu&rft.date=2021-06-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.eissn=2373-7476&rft.volume=8&rft.issue=3&rft.spage=589&rft_id=info:doi/10.1109%2FTCSS.2021.3055823&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2329-924X&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2329-924X&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2329-924X&client=summon