Double Regularization Matrix Factorization Recommendation Algorithm
With the development of social networks, the research of integrated social information recommendation models has received extensive attention. However, most existing social recommendation models are based on the matrix factorization technique which ignore the impact of the relationships between item...
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| Veröffentlicht in: | IEEE access 2019, Vol.7, p.139668-139677 |
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| creator | Du, Ruizhong Lu, Jiaojiao Cai, Hongyun |
| description | With the development of social networks, the research of integrated social information recommendation models has received extensive attention. However, most existing social recommendation models are based on the matrix factorization technique which ignore the impact of the relationships between items on users' interests, resulting in a decline of recommendation accuracy. To solve this problem, this paper proposes a double regularization matrix factorization recommendation algorithm. The algorithm first uses attribute information and manifold learning to calculate similarity. Then, the matrix factorization model is constrained through the regularization of item association relations and user social relations. Experimental results on real datasets show that the proposed method can effectively alleviate problems such as cold start and data sparsity in the recommender system and improve the recommendation accuracy compared with those of existing methods. |
| doi_str_mv | 10.1109/ACCESS.2019.2943600 |
| format | Article |
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However, most existing social recommendation models are based on the matrix factorization technique which ignore the impact of the relationships between items on users' interests, resulting in a decline of recommendation accuracy. To solve this problem, this paper proposes a double regularization matrix factorization recommendation algorithm. The algorithm first uses attribute information and manifold learning to calculate similarity. Then, the matrix factorization model is constrained through the regularization of item association relations and user social relations. 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However, most existing social recommendation models are based on the matrix factorization technique which ignore the impact of the relationships between items on users' interests, resulting in a decline of recommendation accuracy. To solve this problem, this paper proposes a double regularization matrix factorization recommendation algorithm. The algorithm first uses attribute information and manifold learning to calculate similarity. Then, the matrix factorization model is constrained through the regularization of item association relations and user social relations. Experimental results on real datasets show that the proposed method can effectively alleviate problems such as cold start and data sparsity in the recommender system and improve the recommendation accuracy compared with those of existing methods.</description><subject>Algorithms</subject><subject>Collaboration</subject><subject>Correlation</subject><subject>Factorization</subject><subject>item similarity</subject><subject>Machine learning</subject><subject>manifold learning</subject><subject>Manifolds</subject><subject>Manifolds (mathematics)</subject><subject>matrix factorization</subject><subject>recommender system</subject><subject>Recommender systems</subject><subject>Regularization</subject><subject>Social network</subject><subject>Social networking (online)</subject><subject>Social networks</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUE1PwkAQ3RhNJMgv4ELiubjf7R5JBSXBmICeN7PbLZa0LG5Lov56F0uIc5mZN_PeTB5CY4KnhGD1MMvz-WYzpZioKVWcSYyv0IASqRImmLz-V9-iUdvucIwsQiIdoPzRH03tJmu3PdYQqh_oKr-fvEAXqq_JAmznL-DaWd80bl_07azexln30dyhmxLq1o3OeYjeF_O3_DlZvT4t89kqsRxnXWJsQSxJHZZSEsxTwwqagpLARMFKsDbCzDIgDDOHqSyJLZhxgFNjpaAFG6Jlr1t42OlDqBoI39pDpf8AH7YaQlfZ2ulMpNJyApK5ghsKGTUSc8WllaWBVEWt-17rEPzn0bWd3vlj2Mf3NeVCSCI4yeIW67ds8G0bXHm5SrA-ma978_XJfH02P7LGPatyzl0YWcYzpij7BcnPf_k</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Du, Ruizhong</creator><creator>Lu, Jiaojiao</creator><creator>Cai, Hongyun</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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However, most existing social recommendation models are based on the matrix factorization technique which ignore the impact of the relationships between items on users' interests, resulting in a decline of recommendation accuracy. To solve this problem, this paper proposes a double regularization matrix factorization recommendation algorithm. The algorithm first uses attribute information and manifold learning to calculate similarity. Then, the matrix factorization model is constrained through the regularization of item association relations and user social relations. Experimental results on real datasets show that the proposed method can effectively alleviate problems such as cold start and data sparsity in the recommender system and improve the recommendation accuracy compared with those of existing methods.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2019.2943600</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0003-3129-8922</orcidid><oa>free_for_read</oa></addata></record> |
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| source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Algorithms Collaboration Correlation Factorization item similarity Machine learning manifold learning Manifolds Manifolds (mathematics) matrix factorization recommender system Recommender systems Regularization Social network Social networking (online) Social networks |
| title | Double Regularization Matrix Factorization Recommendation Algorithm |
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