Enhancing social collaborative filtering through the application of non-negative matrix factorization and exponential random graph models
Social collaborative filtering recommender systems extend the traditional user-to-item interaction with explicit user-to-user relationships, thereby allowing for a wider exploration of correlations among users and items, that potentially lead to better recommendations. A number of methods have been...
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| Published in: | Data mining and knowledge discovery Vol. 31; no. 4; pp. 1031 - 1059 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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New York
Springer US
01.07.2017
Springer Nature B.V |
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| ISSN: | 1384-5810, 1573-756X |
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| Abstract | Social collaborative filtering recommender systems extend the traditional user-to-item interaction with explicit user-to-user relationships, thereby allowing for a wider exploration of correlations among users and items, that potentially lead to better recommendations. A number of methods have been proposed in the direction of exploring the social network, either locally (i.e. the vicinity of each user) or globally. In this paper, we propose a novel methodology for collaborative filtering social recommendation that tries to combine the merits of both the aforementioned approaches, based on the soft-clustering of the Friend-of-a-Friend (FoaF) network of each user. This task is accomplished by the non-negative factorization of the adjacency matrix of the FoaF graph, while the edge-centric logic of the factorization algorithm is ameliorated by incorporating more general structural properties of the graph, such as the number of edges and stars, through the introduction of the exponential random graph models. The preliminary results obtained reveal the potential of this idea. |
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| AbstractList | Social collaborative filtering recommender systems extend the traditional user-to-item interaction with explicit user-to-user relationships, thereby allowing for a wider exploration of correlations among users and items, that potentially lead to better recommendations. A number of methods have been proposed in the direction of exploring the social network, either locally (i.e. the vicinity of each user) or globally. In this paper, we propose a novel methodology for collaborative filtering social recommendation that tries to combine the merits of both the aforementioned approaches, based on the soft-clustering of the Friend-of-a-Friend (FoaF) network of each user. This task is accomplished by the non-negative factorization of the adjacency matrix of the FoaF graph, while the edge-centric logic of the factorization algorithm is ameliorated by incorporating more general structural properties of the graph, such as the number of edges and stars, through the introduction of the exponential random graph models. The preliminary results obtained reveal the potential of this idea. |
| Author | Alexandridis, Georgios Siolas, Georgios Stafylopatis, Andreas |
| Author_xml | – sequence: 1 givenname: Georgios orcidid: 0000-0002-3611-8292 surname: Alexandridis fullname: Alexandridis, Georgios email: gealexandri@islab.ntua.gr organization: School of Electrical and Computer Engineering, National Technical University of Athens – sequence: 2 givenname: Georgios surname: Siolas fullname: Siolas, Georgios organization: School of Electrical and Computer Engineering, National Technical University of Athens – sequence: 3 givenname: Andreas surname: Stafylopatis fullname: Stafylopatis, Andreas organization: School of Electrical and Computer Engineering, National Technical University of Athens |
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| CitedBy_id | crossref_primary_10_1007_s11257_019_09227_6 crossref_primary_10_1016_j_ins_2020_01_046 crossref_primary_10_1109_TNSE_2023_3258427 crossref_primary_10_1145_3746637 crossref_primary_10_1007_s10044_017_0671_2 crossref_primary_10_1109_ACCESS_2022_3141795 crossref_primary_10_1109_ACCESS_2019_2906769 crossref_primary_10_1155_2022_3150626 crossref_primary_10_3390_fi13020037 crossref_primary_10_32604_cmc_2020_010424 crossref_primary_10_1145_3565575 crossref_primary_10_1109_ACCESS_2021_3054818 crossref_primary_10_3390_a14100281 crossref_primary_10_1016_j_eswa_2021_115170 crossref_primary_10_1109_ACCESS_2022_3175566 crossref_primary_10_1002_ett_3914 crossref_primary_10_1007_s12652_021_03368_6 crossref_primary_10_1080_08839514_2023_2222495 crossref_primary_10_1007_s10618_024_01021_2 crossref_primary_10_1109_ACCESS_2021_3096609 crossref_primary_10_1007_s10799_019_00309_w |
| Cites_doi | 10.1103/PhysRevE.83.066114 10.1016/j.neucom.2014.10.099 10.1103/PhysRevLett.3.77 10.1109/TKDE.2012.51 10.1093/acprof:oso/9780199206650.001.0001 10.1007/978-1-84800-356-9_10 10.1109/TKDE.2005.99 10.1103/PhysRevE.72.026136 10.1103/PhysRevE.70.066117 10.1103/PhysRevE.70.066146 10.1016/j.comcom.2013.06.009 10.1007/978-0-387-85820-3_4 10.1007/978-3-319-14379-8_3 10.1016/j.socnet.2006.08.002 10.1145/1401890.1401944 10.1145/2365952.2365969 10.1145/1864708.1864736 10.1145/1345448.1345459 10.1145/1864708.1864764 10.1145/1557019.1557067 10.1145/1571941.1571977 10.1145/192844.192905 10.1109/ICDM.2008.22 10.1145/1571941.1571978 10.1038/44565 10.1007/978-3-540-68880-8_32 |
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| Keywords | Exponential random graph models Social collaborative filtering Non-negative matrix factorization Recommender systems |
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| Title | Enhancing social collaborative filtering through the application of non-negative matrix factorization and exponential random graph models |
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