A survey of collaborative filtering based social recommender systems
Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest to a user items that might be of interest to her. Recent studies demonstrate that information from social networks can be exploited to improve accuracy of recommendations. In this paper,...
Gespeichert in:
| Veröffentlicht in: | Computer communications Jg. 41; S. 1 - 10 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Kidlington
Elsevier B.V
15.03.2014
Elsevier |
| Schlagworte: | |
| ISSN: | 0140-3664, 1873-703X |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest to a user items that might be of interest to her. Recent studies demonstrate that information from social networks can be exploited to improve accuracy of recommendations. In this paper, we present a survey of collaborative filtering (CF) based social recommender systems. We provide a brief overview over the task of recommender systems and traditional approaches that do not use social network information. We then present how social network information can be adopted by recommender systems as additional input for improved accuracy. We classify CF-based social recommender systems into two categories: matrix factorization based social recommendation approaches and neighborhood based social recommendation approaches. For each category, we survey and compare several representative algorithms. |
|---|---|
| AbstractList | Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest to a user items that might be of interest to her. Recent studies demonstrate that information from social networks can be exploited to improve accuracy of recommendations. In this paper, we present a survey of collaborative filtering (CF) based social recommender systems. We provide a brief overview over the task of recommender systems and traditional approaches that do not use social network information. We then present how social network information can be adopted by recommender systems as additional input for improved accuracy. We classify CF-based social recommender systems into two categories: matrix factorization based social recommendation approaches and neighborhood based social recommendation approaches. For each category, we survey and compare several representative algorithms. |
| Author | Steck, Harald Guo, Yang Liu, Yong Yang, Xiwang |
| Author_xml | – sequence: 1 givenname: Xiwang surname: Yang fullname: Yang, Xiwang email: xy271@nyu.edu organization: Polytechnic Institute of NYU, Brooklyn, NY, USA – sequence: 2 givenname: Yang surname: Guo fullname: Guo, Yang email: Yang.Guo@alcatel-lucent.com organization: Bell Labs, Alcatel-Lucent, Holmdel, NJ, USA – sequence: 3 givenname: Yong surname: Liu fullname: Liu, Yong email: yongliu@poly.edu organization: Polytechnic Institute of NYU, Brooklyn, NY, USA – sequence: 4 givenname: Harald surname: Steck fullname: Steck, Harald email: hsteck@netflix.com organization: Netflix Inc., Los Gatos, CA, USA |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28282340$$DView record in Pascal Francis |
| BookMark | eNqFkE1Lw0AQhhepYKv-Aw-5eEycZNdN4kEo9RMKXhS8LdPJrGxJs7IbC_33rlQ8eFBmYA7zPjPwzMRk8AMLcVZCUUKpL9YF-U3qooJSFqALgPZATMumlnkN8nUiplAqyKXW6kjMYlwDgKprORU38yx-hC3vMm8z8n2PKx9wdFvOrOtHDm54y1YYucuiJ4d9Fjh92vDQccjiLo68iSfi0GIf-fR7HouXu9vnxUO-fLp_XMyXOUmpx7yipqMWrMKqQmsVtC03TIyXlxVqW6aNrPVKUdsAYs1MihB0U60YO1WTPBbn-7vvGAl7G3AgF817cBsMO1M1qaSClFP7HAUfY2D7EynBfBkza7M3Zr6MGdAmGUvY1S-M3Jhc-GEM6Pr_4Os9zEnA1nEwkRwPxJ1LxkbTeff3gU9a8Y1m |
| CitedBy_id | crossref_primary_10_1016_j_comcom_2022_01_020 crossref_primary_10_1007_s10618_017_0504_3 crossref_primary_10_1007_s11740_017_0715_x crossref_primary_10_1109_TKDE_2018_2832132 crossref_primary_10_3390_data4010007 crossref_primary_10_1016_j_physa_2016_11_091 crossref_primary_10_1007_s11042_020_08620_3 crossref_primary_10_1080_14498596_2021_1896392 crossref_primary_10_1109_ACCESS_2019_2933028 crossref_primary_10_1109_TAFFC_2017_2695605 crossref_primary_10_3390_bdcc3010015 crossref_primary_10_1016_j_hsr_2024_100150 crossref_primary_10_1007_s00500_022_07440_x crossref_primary_10_3390_systems11040169 crossref_primary_10_1007_s10844_024_00865_w crossref_primary_10_1016_j_ygeno_2019_01_001 crossref_primary_10_1007_s12652_016_0357_4 crossref_primary_10_1007_s12652_020_01681_0 crossref_primary_10_1109_TBDATA_2020_3034976 crossref_primary_10_3390_app8050704 crossref_primary_10_1016_j_eswa_2024_125223 crossref_primary_10_1155_2022_2520140 crossref_primary_10_1016_S2468_6964_17_30045_9 crossref_primary_10_1007_s11859_017_1228_3 crossref_primary_10_1109_ACCESS_2019_2931756 crossref_primary_10_1007_s42979_025_04095_x crossref_primary_10_1007_s10660_023_09771_9 crossref_primary_10_1007_s11227_016_1717_8 crossref_primary_10_3390_electronics9020266 crossref_primary_10_1016_j_jpdc_2019_10_006 crossref_primary_10_1109_ACCESS_2019_2897760 crossref_primary_10_1016_j_eswa_2019_112955 crossref_primary_10_1016_j_physa_2019_03_113 crossref_primary_10_1007_s11276_018_01901_8 crossref_primary_10_1016_j_knosys_2019_04_005 crossref_primary_10_1016_j_future_2016_10_019 crossref_primary_10_1016_j_elerap_2020_100938 crossref_primary_10_1109_TKDE_2017_2717984 crossref_primary_10_1007_s10791_018_9327_0 crossref_primary_10_1016_j_ijhm_2020_102710 crossref_primary_10_1016_j_eswa_2021_116262 crossref_primary_10_1007_s00521_024_10828_5 crossref_primary_10_1007_s10844_019_00578_5 crossref_primary_10_1016_j_eswa_2022_117035 crossref_primary_10_1016_j_ins_2020_05_071 crossref_primary_10_1080_09540091_2022_2078280 crossref_primary_10_1016_j_cageo_2021_104935 crossref_primary_10_1016_j_comcom_2023_03_018 crossref_primary_10_1016_j_procs_2019_09_399 crossref_primary_10_1080_10447318_2025_2546662 crossref_primary_10_3390_electronics11162630 crossref_primary_10_1007_s00607_018_0631_8 crossref_primary_10_3390_app10217748 crossref_primary_10_1016_j_eswa_2019_04_001 crossref_primary_10_1007_s00521_020_05085_1 crossref_primary_10_1016_j_eswa_2020_113270 crossref_primary_10_1016_j_knosys_2020_105628 crossref_primary_10_1587_transinf_2020BDP0012 crossref_primary_10_1007_s12652_020_01997_x crossref_primary_10_1080_17517575_2017_1287429 crossref_primary_10_1109_JIOT_2024_3506713 crossref_primary_10_22581_muet1982_1804_02 crossref_primary_10_1016_j_neucom_2016_10_082 crossref_primary_10_1016_j_physa_2019_04_257 crossref_primary_10_1080_10919392_2020_1718056 crossref_primary_10_3390_systems11080414 crossref_primary_10_1007_s13278_016_0349_6 crossref_primary_10_3233_IDA_192531 crossref_primary_10_1007_s10639_017_9668_0 crossref_primary_10_1108_K_03_2018_0143 crossref_primary_10_1155_2018_5787406 crossref_primary_10_1016_j_csda_2023_107836 crossref_primary_10_1109_TNSE_2018_2815686 crossref_primary_10_1016_j_eswa_2018_03_006 crossref_primary_10_1109_ACCESS_2025_3532697 crossref_primary_10_1145_3661821 crossref_primary_10_3390_electronics8050506 crossref_primary_10_3390_e20010064 crossref_primary_10_1007_s10844_018_0513_8 crossref_primary_10_3390_electronics11213466 crossref_primary_10_1371_journal_pone_0188747 crossref_primary_10_1109_ACCESS_2019_2892289 crossref_primary_10_1080_09720510_2020_1736318 crossref_primary_10_1016_j_techsoc_2020_101464 crossref_primary_10_3390_info12110435 crossref_primary_10_3390_app11146477 crossref_primary_10_1016_j_ipm_2023_103601 crossref_primary_10_1016_j_physa_2019_122244 crossref_primary_10_1007_s00521_021_06493_7 crossref_primary_10_1016_j_is_2021_101742 crossref_primary_10_1109_ACCESS_2018_2810062 crossref_primary_10_3390_electronics9091496 crossref_primary_10_1016_j_future_2019_01_003 crossref_primary_10_1007_s10462_019_09744_1 crossref_primary_10_1109_ACCESS_2019_2895647 crossref_primary_10_1016_j_engappai_2021_104325 crossref_primary_10_1016_j_ins_2017_07_021 crossref_primary_10_1186_s40064_016_1841_1 crossref_primary_10_1016_j_asoc_2018_11_018 crossref_primary_10_3390_ijgi12020079 crossref_primary_10_1016_j_physa_2019_122255 crossref_primary_10_1016_j_neucom_2015_11_059 crossref_primary_10_1007_s13042_021_01409_2 crossref_primary_10_1016_j_knosys_2017_03_023 crossref_primary_10_1016_j_knosys_2016_01_011 crossref_primary_10_1016_j_comcom_2022_11_011 crossref_primary_10_1016_j_ins_2019_12_038 crossref_primary_10_1016_j_procs_2020_09_283 crossref_primary_10_1007_s10796_022_10262_9 crossref_primary_10_1093_joclec_nhad009 crossref_primary_10_3390_electronics12224564 crossref_primary_10_1109_ACCESS_2018_2890553 crossref_primary_10_1016_j_neucom_2014_08_103 crossref_primary_10_1016_j_ins_2019_07_081 crossref_primary_10_1145_3134728 crossref_primary_10_3233_HIS_240003 crossref_primary_10_1016_j_comcom_2015_11_005 crossref_primary_10_1007_s00500_019_03807_9 crossref_primary_10_1007_s12652_020_02714_4 crossref_primary_10_1080_02522667_2019_1580881 crossref_primary_10_1016_j_eswa_2023_122880 crossref_primary_10_1371_journal_pone_0204434 crossref_primary_10_1016_j_knosys_2017_06_034 crossref_primary_10_1016_j_procs_2017_11_157 crossref_primary_10_1109_ACCESS_2018_2877208 crossref_primary_10_3390_sym16121602 crossref_primary_10_3233_AIS_200585 crossref_primary_10_1145_3767326 crossref_primary_10_1109_TITS_2022_3228293 crossref_primary_10_1287_isre_2021_0281 crossref_primary_10_3390_app15084170 crossref_primary_10_1016_j_ins_2022_05_025 crossref_primary_10_3390_s19020431 crossref_primary_10_1016_j_neunet_2020_01_021 crossref_primary_10_1016_j_knosys_2022_108954 crossref_primary_10_1016_j_knosys_2019_03_032 crossref_primary_10_1145_3469799 crossref_primary_10_1007_s10844_020_00613_w crossref_primary_10_1016_j_neucom_2019_04_079 crossref_primary_10_1016_j_comnet_2017_04_016 crossref_primary_10_1016_j_ins_2019_11_045 crossref_primary_10_1108_OIR_12_2016_0360 crossref_primary_10_1007_s12652_018_0807_2 crossref_primary_10_1016_j_osnem_2020_100070 crossref_primary_10_1016_j_knosys_2016_05_037 crossref_primary_10_1016_j_heliyon_2021_e07397 crossref_primary_10_1016_j_ijinfomgt_2018_10_010 crossref_primary_10_1016_j_procs_2018_10_524 crossref_primary_10_1016_j_jbi_2020_103635 crossref_primary_10_1108_APJML_10_2016_0188 crossref_primary_10_1016_j_amc_2022_127549 crossref_primary_10_1049_sfw2_12118 crossref_primary_10_1002_cpe_5572 crossref_primary_10_1145_3290768_3290775 crossref_primary_10_1016_j_future_2018_05_070 crossref_primary_10_3390_s20072098 crossref_primary_10_1145_3387162 crossref_primary_10_1016_j_jksuci_2020_09_010 crossref_primary_10_1109_ACCESS_2018_2871681 crossref_primary_10_1109_ACCESS_2020_3001210 crossref_primary_10_1016_j_future_2016_05_040 crossref_primary_10_1109_JSYST_2015_2427193 crossref_primary_10_1016_j_eswa_2023_120699 crossref_primary_10_1007_s12351_017_0325_6 crossref_primary_10_1108_OIR_05_2018_0177 crossref_primary_10_1109_TKDE_2022_3187434 crossref_primary_10_1016_j_datak_2023_102142 crossref_primary_10_1007_s13042_017_0778_1 crossref_primary_10_1007_s11036_018_1112_1 crossref_primary_10_1007_s10462_017_9539_5 crossref_primary_10_1016_j_comcom_2016_12_011 crossref_primary_10_1016_j_ins_2017_09_050 crossref_primary_10_1109_ACCESS_2019_2895824 crossref_primary_10_1109_ACCESS_2022_3141795 crossref_primary_10_1016_j_ins_2019_08_009 crossref_primary_10_1007_s13748_017_0133_5 crossref_primary_10_1155_2017_2587069 crossref_primary_10_1080_00207543_2017_1287443 crossref_primary_10_1016_j_is_2015_07_008 crossref_primary_10_1109_TC_2023_3275107 crossref_primary_10_1371_journal_pone_0315533 crossref_primary_10_1016_j_eswa_2022_116963 crossref_primary_10_1016_j_neucom_2023_126441 crossref_primary_10_1007_s11336_023_09926_5 crossref_primary_10_1109_TITS_2021_3125372 crossref_primary_10_1109_TSC_2014_2365797 crossref_primary_10_1007_s11042_017_4620_2 crossref_primary_10_1007_s10796_019_09935_9 crossref_primary_10_1002_cpe_3900 crossref_primary_10_1007_s10115_024_02315_z crossref_primary_10_1142_S2196888819500040 crossref_primary_10_1155_2019_1326831 crossref_primary_10_1007_s10586_017_1414_2 crossref_primary_10_1145_3137597_3137599 crossref_primary_10_1145_3674723 crossref_primary_10_1145_2951952 crossref_primary_10_1007_s10844_024_00906_4 crossref_primary_10_1016_j_jocs_2018_08_007 crossref_primary_10_1016_j_neunet_2018_12_011 crossref_primary_10_1145_3757057 crossref_primary_10_1155_2021_3400943 crossref_primary_10_1145_3473338 crossref_primary_10_1016_j_future_2019_04_018 crossref_primary_10_1109_ACCESS_2018_2842257 crossref_primary_10_1007_s12652_019_01256_8 crossref_primary_10_1016_j_jnca_2020_102579 crossref_primary_10_1016_j_procs_2018_08_263 crossref_primary_10_1111_exsy_13478 crossref_primary_10_1007_s11042_022_12231_5 crossref_primary_10_1109_ACCESS_2023_3276988 crossref_primary_10_1109_TCSS_2017_2772295 crossref_primary_10_4018_IJKSS_2019040102 crossref_primary_10_3233_WEB_190415 crossref_primary_10_1016_j_future_2021_06_008 crossref_primary_10_3390_electronics13142811 crossref_primary_10_1155_2019_3965845 crossref_primary_10_1007_s11042_023_16169_0 crossref_primary_10_1109_ACCESS_2019_2928574 crossref_primary_10_1109_ACCESS_2020_3034716 crossref_primary_10_1016_j_physa_2016_01_051 crossref_primary_10_1016_j_neucom_2018_07_035 crossref_primary_10_3390_pr7050265 crossref_primary_10_1109_TNET_2020_2976927 crossref_primary_10_1016_j_knosys_2021_106817 crossref_primary_10_1155_2022_6958596 crossref_primary_10_1016_j_eswa_2017_01_060 crossref_primary_10_1007_s10489_017_0973_5 crossref_primary_10_1177_0165551519849510 |
| Cites_doi | 10.1145/2339530.2339728 10.1145/2043932.2043972 10.1016/j.eswa.2009.12.061 10.1155/2009/421425 10.1145/1571941.1571978 10.1145/1935826.1935877 10.1145/1273496.1273596 10.1145/1963405.1963481 10.1145/1639714.1639745 10.1038/44565 10.1145/1297231.1297235 10.1109/TKDE.2005.99 10.1145/1835804.1835893 10.1145/2043932.2043965 10.1145/963770.963772 10.1145/2043932.2043947 10.1146/annurev.soc.27.1.415 10.1109/ICDM.2008.22 10.1145/1557019.1557072 10.1007/978-3-540-24747-0_19 10.1109/ICDM.2008.16 10.1145/582415.582418 10.1016/j.dss.2006.11.003 10.1109/TPDS.2012.192 10.1145/1835804.1835895 10.1145/1040830.1040870 10.1145/1557019.1557067 10.1007/978-0-387-85820-3 10.1145/1458082.1458205 10.1145/1864708.1864736 10.1145/1864708.1864731 10.1007/978-3-540-24747-0_17 10.1145/2187836.2187952 10.1145/2043932.2043978 10.1145/2365952.2365969 10.1145/1401890.1401944 |
| ContentType | Journal Article |
| Copyright | 2013 Elsevier B.V. 2015 INIST-CNRS |
| Copyright_xml | – notice: 2013 Elsevier B.V. – notice: 2015 INIST-CNRS |
| DBID | AAYXX CITATION IQODW |
| DOI | 10.1016/j.comcom.2013.06.009 |
| DatabaseName | CrossRef Pascal-Francis |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Applied Sciences |
| EISSN | 1873-703X |
| EndPage | 10 |
| ExternalDocumentID | 28282340 10_1016_j_comcom_2013_06_009 S0140366413001722 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 77K 8P~ 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXUO AAYFN ABBOA ABFNM ABMAC ABXDB ABYKQ ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ IHE J1W JJJVA KOM LG9 M41 MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG ROL RPZ RXW SDF SDG SDP SES SPC SPCBC SST SSV SSZ T5K WH7 ZMT ~G- 07C 29F 77I 9DU AAQXK AATTM AAXKI AAYWO AAYXX ABJNI ABWVN ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AI. AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS F0J FEDTE FGOYB HLZ HVGLF HZ~ R2- SBC SEW TAE UHS VH1 VOH WUQ XPP ZY4 ~HD ABTAH BNPGV IQODW SSH |
| ID | FETCH-LOGICAL-c336t-2c8dc90f4a22aff4099e8ecea552a6f10f4376b4c980aa7eec4ca0682bead47c3 |
| ISICitedReferencesCount | 324 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000334484700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0140-3664 |
| IngestDate | Wed Apr 02 07:26:07 EDT 2025 Tue Nov 18 22:42:21 EST 2025 Sat Nov 29 04:00:59 EST 2025 Fri Feb 23 02:23:47 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Collaborative filtering Social network Recommender system Matrix factorization Recommendation |
| Language | English |
| License | CC BY 4.0 |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c336t-2c8dc90f4a22aff4099e8ecea552a6f10f4376b4c980aa7eec4ca0682bead47c3 |
| PageCount | 10 |
| ParticipantIDs | pascalfrancis_primary_28282340 crossref_primary_10_1016_j_comcom_2013_06_009 crossref_citationtrail_10_1016_j_comcom_2013_06_009 elsevier_sciencedirect_doi_10_1016_j_comcom_2013_06_009 |
| PublicationCentury | 2000 |
| PublicationDate | 2014-03-15 |
| PublicationDateYYYYMMDD | 2014-03-15 |
| PublicationDate_xml | – month: 03 year: 2014 text: 2014-03-15 day: 15 |
| PublicationDecade | 2010 |
| PublicationPlace | Kidlington |
| PublicationPlace_xml | – name: Kidlington |
| PublicationTitle | Computer communications |
| PublicationYear | 2014 |
| Publisher | Elsevier B.V Elsevier |
| Publisher_xml | – name: Elsevier B.V – name: Elsevier |
| References | McPherson, Smith-Lovin, Cook (b0225) 2001; 27 M. Jahrer, A. Toscher, R. Legenstein, Combining predictions for accurate recommender systems, in: Proc. of KDD ’10, Washington, DC, USA, 2010, pp. 693–702. T. DuBois, J. Golbeck, J. Kleint, A. Srinivasan, Improving recommendation accuracy by clustering social neworks with trust, in: Proceedings of the ACM RecSys 2009 Workshop on Recommender Systems and the Social Web, Oct. 2009. X. Yang, H. Steck, Y.Guo, Y. Liu, On Top-k recommendation using social networks, in: ACM Conference on Recommender Systems (RecSys’12), 2012. S.M. McNee, J. Riedl, J.A. Konstan, Being accurate is not enough: how accuracy metrics have hurt recommender systems, in: Proceeding CHI EA ’06 CHI ’06 Extended Abstracts on Human factors in Computing Systems. F. Ricci, L. Rokach, B. Shapira, P.B. (Eds.), Kantor Recommender Systems Handbook, first ed., 2011, XXX, 842 p. 20 illus H. Steck, Training and testing of recommender systems on data missing not at random, in: ACM Conference on Knowledge Discovery and Data Mining, 2010, pp. 713–722. Ziegler, Golbeck (b0245) 2007; 43 YouTube, available at R. Salakhutdinov, A. Mnih, Probabilistic matrix factorization, in: NIPS 2008, vol. 20. J. O’Donovan, B. Smyth, Trust in recommender systems, in: Proceedings of the 10th International Conference on Intelligent User Interfaces, 2005. Lee, Seung (b0160) 1999; 401 Keshavan, Montanari, Oh (b0055) 2010; 11 Twitter, available at Sarwar, Karypis, Konstan, Reidl (b0310) 2001 Adomavicius, Tuzhilin (b0005) June 2005; 17 Y. Koren, Collaborative filtering with temporal dynamics, in: Proc. of KDD ’09, Paris, France, 2009, pp. 447–456. V. Vasuki, N. Natarajan, Z. Lu, I. Dhillon, Affiliation recommendation using auxiliary networks, in: ACM Conference on Recommender Systems (RecSys’2010), 2010. . M.A. Tayebi, M. Jamali, M. Ester, U. Glasser, R. Frank, CrimeWalker: a recommendation model for suspect investigation, in: RecSys ’11 Proceedings of the fifth ACM conference on Recommender systems, 2011. J.A. Golbeck, Computing and applying trust in web-based social networks, PhD. Dissertation, 2005. Su, Khoshgoftaar (b0015) 2009; 2009 S. Funk, Netflix update: try this at home, 2006 P. Massa, B. Bhattacharjee, Using trust in recommender systems: an experimental analysis, in Proceedings of iTrust2004 International Conference, 2004, pp. 221–235. H. Ma, I. King, M.R. Lyu, Learning to recommend with social trust ensemble, in: ACM Conference on Research and Development in Information Retrieval (SIGIR), 2009. X. Yang, H. Steck, Y. Liu, Circle-based recommendation in online social networks, in: Proceedings of 2012 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12). J. Noel, S. Sanner, K.-N. Tran, P. Christen, L. Xie, E. Bonilla, E. Abbasnejad, N.D. Penna, New objective functions for social collaborative filtering, in: Proceedings of the 21st International Conference on World Wide Web (www2012). Y. Xin, H. Steck, Multi-value probabilistic matrix factorization for IP-TV recommendations, in: ACM Conference on Recommender Systems (RecSys), 2011. P. Bedi, H. Kaur, S. Marwaha, Trust based recommender system for semantic web, in: Proc. of IJCAI ’07, 2007, pp. 2677–2682. K. Sarda, P. Gupta, D. Mukherjee, S. Padhy, H. Saran, A distributed trust-based recommendation system on social network, in: Proceedings of the 10th Second IEEE Workshop on Hot Topics in Web Systems and Technologies, 2008. H. Ma, H. Yang, M.R. Lyu, I. King, Sorec: social recommendation using probabilistic matrix factorization, in: International Conference on Information and Knowledge Management (CIKM), 2008. Epinions, available at M. Jamali, M. Ester, Trustwalker: a random walk model for combining trust-based and item-based recommendation, in: ACM Conference on Knowledge Discovery and Data Mining (KDD), 2009. M. Jamali, M. Ester, A matrix factorization technique with trust propagation for recommendation, in: Social Networks Proceedings of the 2010 ACM conference on Recommender systems(RecSys), 2010. S.-H. Yang, B. Long, A.J. Smola, N. Sadagopan, Z. Zheng, H. Zha, Like alike: joint friendship and interest propagation in social networks, in: Proceedings of the 20th International Conference on World Wide Web (www2011). L. Yu, R. Pan, Z. Li, Adaptive social similarities for recommender systems, in: RecSys ’11 Proceedings of the Fifth ACM Conference on Recommender Systems, 2011. Google Plus Flixster R. Salakhutdinov, A. Mnih, G. Hinton, Restricted Boltzmann machines for collaborative filtering, in: International Conference on Machine Learning (ICML), 2007. Jarvelin, Kekalainen (b0315) 2002; 20 P. Symeonidis, E. Tiakas, Y. Manolopoulos, Product recommendation and rating prediction based on multi-modal social networks, in: Proceedings of the 5th ACM Conference in Recommender Systems (RecSys’2011), Chicago, IL, 2011. Y. Koren, Factorization meets the neighborhood: a multifaceted collaborative filtering model, in: ACM Conference on Knowledge Discovery and Data Mining, 2008, pp. 426–34. C.-N. Ziegler, G. Lausen, Analyzing correlation between trust and user similarity in online communities, in: Proceedings of Second International Conference on Trust Management, 2004, pp. 251–265. R. Pan, Y. Zhou, B. Cao, N. Liu, R. Lukose, M. Scholz, Q. Yang, One-class collaborative filtering, in: IEEE International Conference on Data Mining (ICDM), 2008. H. Ma, D. Zhou, C. Liu, M.R. Lyu, I. King, Recommender systems with social regularization, in: ACM International Conference on Web Search and Data Mining (WSDM), 2011. Y. Hu, Y. Koren, C. Volinsky, Collaborative filtering for implicit feedback datasets, in: IEEE International Conference on Data Mining (ICDM), 2008. Liu, Lee (b0345) 2010; 37 NetflixPrize, available at P. Massa, P. Avesani, Controversial users demand local trust metrics: an experimental study on epinions.com community, in: Proceedings of the 25th American Association for Artificial Intelligence Conference, 2005. A. Paterek, Improving regularized singular value decomposition for collaborative filtering, in: KDDCup, 2007. Walter, Battiston, Schweitzer (b0275) 2007; 16 S. Deerwester, S. Dumais, G. Furnas, R. Harshman, T. Landauer, K. Lochbaum, L. Streeter et al., Latent semantic analysis/ indexing Herlocker, Konstan, Terveen, Riedl (b0390) 2004; 22 J. Golbeck, J. Hendler, FilmTrust: movie recommendations using trust in web-based social networks, in: Proceedings of the IEEE Consumer Communications and Networking Conference, Jan. 2006, pp. 282–286. M. Jamali, M. Ester, Using a trust network to improve top-n recommendation, in: ACM Conference on Recommender Systems (RecSys), 2009. X. Yang, Y. Guo Y. Liu, Bayesian-inference based recommendation in online social networks, in: IEEE Transactions on Parallel and Distributed Systems (TPDS), 2013, pp. 642–651. P. Massa, P. Avesani, Trust-aware recommender systems, in: Proceedings of the 2007 ACM Conference on Recommender Systems, 2007. Yuan, Zhao, Chen, Liu, Ding, Zhang, Zheng (b0370) 2009 10.1016/j.comcom.2013.06.009_b0105 Jarvelin (10.1016/j.comcom.2013.06.009_b0315) 2002; 20 Ziegler (10.1016/j.comcom.2013.06.009_b0245) 2007; 43 10.1016/j.comcom.2013.06.009_b0305 10.1016/j.comcom.2013.06.009_b0140 10.1016/j.comcom.2013.06.009_b0260 10.1016/j.comcom.2013.06.009_b0065 10.1016/j.comcom.2013.06.009_b0340 10.1016/j.comcom.2013.06.009_b0185 10.1016/j.comcom.2013.06.009_b0265 10.1016/j.comcom.2013.06.009_b0385 McPherson (10.1016/j.comcom.2013.06.009_b0225) 2001; 27 10.1016/j.comcom.2013.06.009_b0300 Sarwar (10.1016/j.comcom.2013.06.009_b0310) 2001 10.1016/j.comcom.2013.06.009_b0180 10.1016/j.comcom.2013.06.009_b0380 10.1016/j.comcom.2013.06.009_b0060 Herlocker (10.1016/j.comcom.2013.06.009_b0390) 2004; 22 10.1016/j.comcom.2013.06.009_b0115 10.1016/j.comcom.2013.06.009_b0235 10.1016/j.comcom.2013.06.009_b0195 10.1016/j.comcom.2013.06.009_b0230 Liu (10.1016/j.comcom.2013.06.009_b0345) 2010; 37 Adomavicius (10.1016/j.comcom.2013.06.009_b0005) 2005; 17 10.1016/j.comcom.2013.06.009_b0350 10.1016/j.comcom.2013.06.009_b0155 10.1016/j.comcom.2013.06.009_b0110 10.1016/j.comcom.2013.06.009_b0355 Su (10.1016/j.comcom.2013.06.009_b0015) 2009; 2009 10.1016/j.comcom.2013.06.009_b0190 10.1016/j.comcom.2013.06.009_b0270 10.1016/j.comcom.2013.06.009_b0325 10.1016/j.comcom.2013.06.009_b0360 10.1016/j.comcom.2013.06.009_b0040 Walter (10.1016/j.comcom.2013.06.009_b0275) 2007; 16 10.1016/j.comcom.2013.06.009_b0285 10.1016/j.comcom.2013.06.009_b0240 10.1016/j.comcom.2013.06.009_b0320 10.1016/j.comcom.2013.06.009_b0165 10.1016/j.comcom.2013.06.009_b0365 10.1016/j.comcom.2013.06.009_b0080 10.1016/j.comcom.2013.06.009_b0280 10.1016/j.comcom.2013.06.009_b0335 10.1016/j.comcom.2013.06.009_b0215 10.1016/j.comcom.2013.06.009_b0250 10.1016/j.comcom.2013.06.009_b0010 10.1016/j.comcom.2013.06.009_b0175 10.1016/j.comcom.2013.06.009_b0130 10.1016/j.comcom.2013.06.009_b0295 Lee (10.1016/j.comcom.2013.06.009_b0160) 1999; 401 10.1016/j.comcom.2013.06.009_b0375 10.1016/j.comcom.2013.06.009_b0330 10.1016/j.comcom.2013.06.009_b0135 10.1016/j.comcom.2013.06.009_b0255 10.1016/j.comcom.2013.06.009_b0290 10.1016/j.comcom.2013.06.009_b0170 Keshavan (10.1016/j.comcom.2013.06.009_b0055) 2010; 11 Yuan (10.1016/j.comcom.2013.06.009_b0370) 2009 |
| References_xml | – volume: 43 start-page: 460 year: 2007 end-page: 475 ident: b0245 article-title: Investigating interactions of trust and interest similarity publication-title: Decision Support Systems – volume: 20 start-page: 422C year: 2002 end-page: 446 ident: b0315 article-title: Cumulated gain-based evaluation of ir techniques publication-title: ACM Transactions on Information Systems – reference: Y. Hu, Y. Koren, C. Volinsky, Collaborative filtering for implicit feedback datasets, in: IEEE International Conference on Data Mining (ICDM), 2008. – reference: J. Golbeck, J. Hendler, FilmTrust: movie recommendations using trust in web-based social networks, in: Proceedings of the IEEE Consumer Communications and Networking Conference, Jan. 2006, pp. 282–286. – reference: P. Symeonidis, E. Tiakas, Y. Manolopoulos, Product recommendation and rating prediction based on multi-modal social networks, in: Proceedings of the 5th ACM Conference in Recommender Systems (RecSys’2011), Chicago, IL, 2011. – reference: X. Yang, H. Steck, Y.Guo, Y. Liu, On Top-k recommendation using social networks, in: ACM Conference on Recommender Systems (RecSys’12), 2012. – reference: F. Ricci, L. Rokach, B. Shapira, P.B. (Eds.), Kantor Recommender Systems Handbook, first ed., 2011, XXX, 842 p. 20 illus – volume: 37 start-page: 4772 year: 2010 end-page: 4778 ident: b0345 article-title: Use of social network information to enhance collaborative filtering performance publication-title: Expert System Applications – reference: S.M. McNee, J. Riedl, J.A. Konstan, Being accurate is not enough: how accuracy metrics have hurt recommender systems, in: Proceeding CHI EA ’06 CHI ’06 Extended Abstracts on Human factors in Computing Systems. – year: 2009 ident: b0370 article-title: Augmenting collaborative recommender by fusing explicit social relationships – reference: S. Funk, Netflix update: try this at home, 2006, – reference: J. O’Donovan, B. Smyth, Trust in recommender systems, in: Proceedings of the 10th International Conference on Intelligent User Interfaces, 2005. – reference: YouTube, available at – volume: 17 year: June 2005 ident: b0005 article-title: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions publication-title: IEEE Transactions on Knowledge and Data Engineering – reference: H. Steck, Training and testing of recommender systems on data missing not at random, in: ACM Conference on Knowledge Discovery and Data Mining, 2010, pp. 713–722. – reference: Twitter, available at – reference: NetflixPrize, available at – reference: M.A. Tayebi, M. Jamali, M. Ester, U. Glasser, R. Frank, CrimeWalker: a recommendation model for suspect investigation, in: RecSys ’11 Proceedings of the fifth ACM conference on Recommender systems, 2011. – volume: 2009 start-page: 19 year: 2009 ident: b0015 article-title: A survey of collaborative filtering techniques publication-title: Advances in Artificial Intelligence – volume: 27 start-page: 415 year: 2001 end-page: 444 ident: b0225 article-title: Birds of a Feather: homophily in social networks publication-title: Annual Review of Sociology – reference: S.-H. Yang, B. Long, A.J. Smola, N. Sadagopan, Z. Zheng, H. Zha, Like alike: joint friendship and interest propagation in social networks, in: Proceedings of the 20th International Conference on World Wide Web (www2011). – reference: Google Plus, – reference: H. Ma, H. Yang, M.R. Lyu, I. King, Sorec: social recommendation using probabilistic matrix factorization, in: International Conference on Information and Knowledge Management (CIKM), 2008. – reference: M. Jahrer, A. Toscher, R. Legenstein, Combining predictions for accurate recommender systems, in: Proc. of KDD ’10, Washington, DC, USA, 2010, pp. 693–702. – reference: R. Pan, Y. Zhou, B. Cao, N. Liu, R. Lukose, M. Scholz, Q. Yang, One-class collaborative filtering, in: IEEE International Conference on Data Mining (ICDM), 2008. – reference: H. Ma, D. Zhou, C. Liu, M.R. Lyu, I. King, Recommender systems with social regularization, in: ACM International Conference on Web Search and Data Mining (WSDM), 2011. – reference: X. Yang, Y. Guo Y. Liu, Bayesian-inference based recommendation in online social networks, in: IEEE Transactions on Parallel and Distributed Systems (TPDS), 2013, pp. 642–651. – reference: M. Jamali, M. Ester, Using a trust network to improve top-n recommendation, in: ACM Conference on Recommender Systems (RecSys), 2009. – reference: M. Jamali, M. Ester, Trustwalker: a random walk model for combining trust-based and item-based recommendation, in: ACM Conference on Knowledge Discovery and Data Mining (KDD), 2009. – reference: L. Yu, R. Pan, Z. Li, Adaptive social similarities for recommender systems, in: RecSys ’11 Proceedings of the Fifth ACM Conference on Recommender Systems, 2011. – reference: K. Sarda, P. Gupta, D. Mukherjee, S. Padhy, H. Saran, A distributed trust-based recommendation system on social network, in: Proceedings of the 10th Second IEEE Workshop on Hot Topics in Web Systems and Technologies, 2008. – volume: 16 start-page: 1573 year: 2007 end-page: 7454 ident: b0275 article-title: A model of a trust-based recommendation system on a social network publication-title: Autonomous Agents and Multi-Agent Systems – reference: Y. Koren, Factorization meets the neighborhood: a multifaceted collaborative filtering model, in: ACM Conference on Knowledge Discovery and Data Mining, 2008, pp. 426–34. – reference: P. Bedi, H. Kaur, S. Marwaha, Trust based recommender system for semantic web, in: Proc. of IJCAI ’07, 2007, pp. 2677–2682. – reference: J. Noel, S. Sanner, K.-N. Tran, P. Christen, L. Xie, E. Bonilla, E. Abbasnejad, N.D. Penna, New objective functions for social collaborative filtering, in: Proceedings of the 21st International Conference on World Wide Web (www2012). – volume: 401 start-page: 788 year: 1999 end-page: 791 ident: b0160 article-title: Learning the parts of objects by non-negative matrix factorization publication-title: Nature – reference: C.-N. Ziegler, G. Lausen, Analyzing correlation between trust and user similarity in online communities, in: Proceedings of Second International Conference on Trust Management, 2004, pp. 251–265. – reference: J.A. Golbeck, Computing and applying trust in web-based social networks, PhD. Dissertation, 2005. – reference: Facebook, – reference: X. Yang, H. Steck, Y. Liu, Circle-based recommendation in online social networks, in: Proceedings of 2012 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12). – volume: 11 start-page: 2057 year: 2010 end-page: 2078 ident: b0055 article-title: Matrix completion from noisy entries publication-title: Journal of Machine Learning Research – reference: Y. Koren, Collaborative filtering with temporal dynamics, in: Proc. of KDD ’09, Paris, France, 2009, pp. 447–456. – reference: P. Massa, P. Avesani, Trust-aware recommender systems, in: Proceedings of the 2007 ACM Conference on Recommender Systems, 2007. – start-page: 285C year: 2001 end-page: 295 ident: b0310 article-title: Item-based collaborative filtering recommendation algorithms publication-title: WWW’ 01: Proceedings of the 10th International Conference on World Wide Web – reference: A. Paterek, Improving regularized singular value decomposition for collaborative filtering, in: KDDCup, 2007. – reference: M. Jamali, M. Ester, A matrix factorization technique with trust propagation for recommendation, in: Social Networks Proceedings of the 2010 ACM conference on Recommender systems(RecSys), 2010. – reference: R. Salakhutdinov, A. Mnih, Probabilistic matrix factorization, in: NIPS 2008, vol. 20. – reference: Flixster, – reference: S. Deerwester, S. Dumais, G. Furnas, R. Harshman, T. Landauer, K. Lochbaum, L. Streeter et al., Latent semantic analysis/ indexing, – reference: Y. Xin, H. Steck, Multi-value probabilistic matrix factorization for IP-TV recommendations, in: ACM Conference on Recommender Systems (RecSys), 2011. – reference: Epinions, available at – reference: T. DuBois, J. Golbeck, J. Kleint, A. Srinivasan, Improving recommendation accuracy by clustering social neworks with trust, in: Proceedings of the ACM RecSys 2009 Workshop on Recommender Systems and the Social Web, Oct. 2009. – reference: . – reference: P. Massa, P. Avesani, Controversial users demand local trust metrics: an experimental study on epinions.com community, in: Proceedings of the 25th American Association for Artificial Intelligence Conference, 2005. – reference: H. Ma, I. King, M.R. Lyu, Learning to recommend with social trust ensemble, in: ACM Conference on Research and Development in Information Retrieval (SIGIR), 2009. – reference: R. Salakhutdinov, A. Mnih, G. Hinton, Restricted Boltzmann machines for collaborative filtering, in: International Conference on Machine Learning (ICML), 2007. – reference: V. Vasuki, N. Natarajan, Z. Lu, I. Dhillon, Affiliation recommendation using auxiliary networks, in: ACM Conference on Recommender Systems (RecSys’2010), 2010. – reference: P. Massa, B. Bhattacharjee, Using trust in recommender systems: an experimental analysis, in Proceedings of iTrust2004 International Conference, 2004, pp. 221–235. – volume: 22 start-page: 5 year: 2004 end-page: 53 ident: b0390 article-title: Evaluating collaborative filtering recommender systems publication-title: ACM Transactions on Information Systems – ident: 10.1016/j.comcom.2013.06.009_b0360 doi: 10.1145/2339530.2339728 – ident: 10.1016/j.comcom.2013.06.009_b0115 doi: 10.1145/2043932.2043972 – volume: 16 start-page: 1573 issue: 1 year: 2007 ident: 10.1016/j.comcom.2013.06.009_b0275 article-title: A model of a trust-based recommendation system on a social network publication-title: Autonomous Agents and Multi-Agent Systems – volume: 37 start-page: 4772 issue: 7 year: 2010 ident: 10.1016/j.comcom.2013.06.009_b0345 article-title: Use of social network information to enhance collaborative filtering performance publication-title: Expert System Applications doi: 10.1016/j.eswa.2009.12.061 – ident: 10.1016/j.comcom.2013.06.009_b0385 – volume: 2009 start-page: 19 year: 2009 ident: 10.1016/j.comcom.2013.06.009_b0015 article-title: A survey of collaborative filtering techniques publication-title: Advances in Artificial Intelligence doi: 10.1155/2009/421425 – ident: 10.1016/j.comcom.2013.06.009_b0330 doi: 10.1145/1571941.1571978 – ident: 10.1016/j.comcom.2013.06.009_b0335 doi: 10.1145/1935826.1935877 – volume: 11 start-page: 2057 year: 2010 ident: 10.1016/j.comcom.2013.06.009_b0055 article-title: Matrix completion from noisy entries publication-title: Journal of Machine Learning Research – ident: 10.1016/j.comcom.2013.06.009_b0140 doi: 10.1145/1273496.1273596 – ident: 10.1016/j.comcom.2013.06.009_b0285 – ident: 10.1016/j.comcom.2013.06.009_b0350 doi: 10.1145/1963405.1963481 – ident: 10.1016/j.comcom.2013.06.009_b0300 doi: 10.1145/1639714.1639745 – volume: 401 start-page: 788 year: 1999 ident: 10.1016/j.comcom.2013.06.009_b0160 article-title: Learning the parts of objects by non-negative matrix factorization publication-title: Nature doi: 10.1038/44565 – ident: 10.1016/j.comcom.2013.06.009_b0255 doi: 10.1145/1297231.1297235 – start-page: 285C year: 2001 ident: 10.1016/j.comcom.2013.06.009_b0310 article-title: Item-based collaborative filtering recommendation algorithms – ident: 10.1016/j.comcom.2013.06.009_b0195 – volume: 17 issue: 6 year: 2005 ident: 10.1016/j.comcom.2013.06.009_b0005 article-title: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions publication-title: IEEE Transactions on Knowledge and Data Engineering doi: 10.1109/TKDE.2005.99 – ident: 10.1016/j.comcom.2013.06.009_b0040 doi: 10.1145/1835804.1835893 – ident: 10.1016/j.comcom.2013.06.009_b0305 doi: 10.1145/2043932.2043965 – ident: 10.1016/j.comcom.2013.06.009_b0080 – volume: 22 start-page: 5 issue: 1 year: 2004 ident: 10.1016/j.comcom.2013.06.009_b0390 article-title: Evaluating collaborative filtering recommender systems publication-title: ACM Transactions on Information Systems doi: 10.1145/963770.963772 – ident: 10.1016/j.comcom.2013.06.009_b0380 doi: 10.1145/2043932.2043947 – volume: 27 start-page: 415 issue: 1 year: 2001 ident: 10.1016/j.comcom.2013.06.009_b0225 article-title: Birds of a Feather: homophily in social networks publication-title: Annual Review of Sociology doi: 10.1146/annurev.soc.27.1.415 – ident: 10.1016/j.comcom.2013.06.009_b0110 doi: 10.1109/ICDM.2008.22 – ident: 10.1016/j.comcom.2013.06.009_b0065 doi: 10.1145/1557019.1557072 – ident: 10.1016/j.comcom.2013.06.009_b0180 – ident: 10.1016/j.comcom.2013.06.009_b0265 – ident: 10.1016/j.comcom.2013.06.009_b0230 – ident: 10.1016/j.comcom.2013.06.009_b0135 – ident: 10.1016/j.comcom.2013.06.009_b0240 doi: 10.1007/978-3-540-24747-0_19 – ident: 10.1016/j.comcom.2013.06.009_b0130 doi: 10.1109/ICDM.2008.16 – ident: 10.1016/j.comcom.2013.06.009_b0165 – ident: 10.1016/j.comcom.2013.06.009_b0190 – volume: 20 start-page: 422C issue: 4 year: 2002 ident: 10.1016/j.comcom.2013.06.009_b0315 article-title: Cumulated gain-based evaluation of ir techniques publication-title: ACM Transactions on Information Systems doi: 10.1145/582415.582418 – volume: 43 start-page: 460 issue: 2 year: 2007 ident: 10.1016/j.comcom.2013.06.009_b0245 article-title: Investigating interactions of trust and interest similarity publication-title: Decision Support Systems doi: 10.1016/j.dss.2006.11.003 – ident: 10.1016/j.comcom.2013.06.009_b0290 doi: 10.1109/TPDS.2012.192 – ident: 10.1016/j.comcom.2013.06.009_b0105 doi: 10.1145/1835804.1835895 – year: 2009 ident: 10.1016/j.comcom.2013.06.009_b0370 – ident: 10.1016/j.comcom.2013.06.009_b0235 – ident: 10.1016/j.comcom.2013.06.009_b0260 – ident: 10.1016/j.comcom.2013.06.009_b0155 – ident: 10.1016/j.comcom.2013.06.009_b0270 doi: 10.1145/1040830.1040870 – ident: 10.1016/j.comcom.2013.06.009_b0295 doi: 10.1145/1557019.1557067 – ident: 10.1016/j.comcom.2013.06.009_b0170 – ident: 10.1016/j.comcom.2013.06.009_b0010 doi: 10.1007/978-0-387-85820-3 – ident: 10.1016/j.comcom.2013.06.009_b0325 doi: 10.1145/1458082.1458205 – ident: 10.1016/j.comcom.2013.06.009_b0320 doi: 10.1145/1864708.1864736 – ident: 10.1016/j.comcom.2013.06.009_b0375 doi: 10.1145/1864708.1864731 – ident: 10.1016/j.comcom.2013.06.009_b0185 – ident: 10.1016/j.comcom.2013.06.009_b0250 doi: 10.1007/978-3-540-24747-0_17 – ident: 10.1016/j.comcom.2013.06.009_b0355 doi: 10.1145/2187836.2187952 – ident: 10.1016/j.comcom.2013.06.009_b0280 – ident: 10.1016/j.comcom.2013.06.009_b0340 doi: 10.1145/2043932.2043978 – ident: 10.1016/j.comcom.2013.06.009_b0365 doi: 10.1145/2365952.2365969 – ident: 10.1016/j.comcom.2013.06.009_b0060 doi: 10.1145/1401890.1401944 – ident: 10.1016/j.comcom.2013.06.009_b0215 – ident: 10.1016/j.comcom.2013.06.009_b0175 |
| SSID | ssj0004773 |
| Score | 2.5715852 |
| Snippet | Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest to a user items that might be of interest to... |
| SourceID | pascalfrancis crossref elsevier |
| SourceType | Index Database Enrichment Source Publisher |
| StartPage | 1 |
| SubjectTerms | Applied sciences Collaborative filtering Computer science; control theory; systems Computer systems and distributed systems. User interface Exact sciences and technology Recommender system Social network Software |
| Title | A survey of collaborative filtering based social recommender systems |
| URI | https://dx.doi.org/10.1016/j.comcom.2013.06.009 |
| Volume | 41 |
| WOSCitedRecordID | wos000334484700001&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-703X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004773 issn: 0140-3664 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwELeg4wGEJr4mBtvkB95QpMRxYvuxgqGBpgmJIXVPkePYUieWVk07xn_P-SNOxgTbHniJqmvjuL7z3fnyuzuE3uWU1CIXdVJQWSRUK5rUpRFJVsO0CwJE4RKFj9nJCZ_NxNcAt-1cOwHWtvzqSiz_K6uBBsy2qbP3YHccFAjwGZgOV2A7XO_E-On7brO61L88YDxy2Vb3nttX4zY2YG1XjJfbM_HFhWspFwo7d2OXte_7YNHnQy5JdMXPQsB5Nv8pgxW0cJ6NC8GejUjH840jLQbSt7X22vhI2sjYOAKRUQvB8jmYMSgJurz01ch7rerLWQW1mI3sq0ex3tDcPohwbhfewnjgObkrrJqKwVL1b-f_MGARVtgj1s4rP0plR6kccE88RFuEFYJP0Nb08-Hsy5A8yzwMof8TfX6lAwHenM3f_JenS9nBrjK-HcrIRzl9hrbD4QJPvVA8Rw90-wI9GZWcfIk-TrEXD7ww-Jp44Cge2IkH9uKBR-KBg3i8Qt8_HZ5-OEpCJ41E5Xm5TojijRKpoZIQaQyc6YXmWmlZFESWJoNvwNDUVAmeSsk0bFkl05ITu2MpU_kOmrSLVr9GWDRNqsGnM-Db0owLXkhWKqYa2khTlnIX5f36VCqUmbfdTn5U_-LOLkriXUtfZuWW37N-6avgKnoXsAJ5uuXOg2ucio-zsQeS0_TNPafyFj0eNsUemqxXG72PHqnL9bxbHQRp-w1j9pqI |
| linkProvider | Elsevier |
| 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+survey+of+collaborative+filtering+based+social+recommender+systems&rft.jtitle=Computer+communications&rft.au=Yang%2C+Xiwang&rft.au=Guo%2C+Yang&rft.au=Liu%2C+Yong&rft.au=Steck%2C+Harald&rft.date=2014-03-15&rft.issn=0140-3664&rft.volume=41&rft.spage=1&rft.epage=10&rft_id=info:doi/10.1016%2Fj.comcom.2013.06.009&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_comcom_2013_06_009 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0140-3664&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0140-3664&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0140-3664&client=summon |