Implicit stochastic gradient descent method for cross-domain recommendation system
DC Field | Value | Language |
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dc.contributor.author | Vo N.D. | - |
dc.contributor.author | Hong M. | - |
dc.contributor.author | Jung, Jason J. | - |
dc.date.available | 2020-06-18T07:20:17Z | - |
dc.date.issued | 2020-05 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.issn | 1424-3210 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/40808 | - |
dc.description.abstract | The previous recommendation system applied the matrix factorization collaborative filtering (MFCF) technique to only single domains. Due to data sparsity, this approach has a limitation in overcoming the cold-start problem. Thus, in this study, we focus on discovering latent features from domains to understand the relationships between domains (called domain coherence). This approach uses potential knowledge of the source domain to improve the quality of the target domain recommendation. In this paper, we consider applying MFCF to multiple domains. Mainly, by adopting the implicit stochastic gradient descent algorithm to optimize the objective function for prediction, multiple matrices from different domains are consolidated inside the cross-domain recommendation system (CDRS). Additionally, we design a conceptual framework for CDRS, which applies to different industrial scenarios for recommenders across domains. Moreover, an experiment is devised to validate the proposed method. By using a real-world dataset gathered from Amazon Food and MovieLens, experimental results show that the proposed method improves 15.2% and 19.7% in terms of computation time and MSE over other methods on a utility matrix. Notably, a much lower convergence value of the loss function has been obtained from the experiment. Furthermore, a critical analysis of the obtained results shows that there is a dynamic balance between prediction accuracy and computational complexity. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI AG | - |
dc.title | Implicit stochastic gradient descent method for cross-domain recommendation system | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/s20092510 | - |
dc.identifier.bibliographicCitation | Sensors (Switzerland), v.20, no.9 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.wosid | 000537106200074 | - |
dc.identifier.scopusid | 2-s2.0-85084276294 | - |
dc.citation.number | 9 | - |
dc.citation.title | Sensors (Switzerland) | - |
dc.citation.volume | 20 | - |
dc.type.docType | Article | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | Convex optimization | - |
dc.subject.keywordAuthor | Cross-domain | - |
dc.subject.keywordAuthor | Implicit update | - |
dc.subject.keywordAuthor | Inner approximation | - |
dc.subject.keywordAuthor | Recommendation system | - |
dc.subject.keywordAuthor | User rating consolidation | - |
dc.subject.keywordPlus | Collaborative filtering | - |
dc.subject.keywordPlus | Factorization | - |
dc.subject.keywordPlus | Gradient methods | - |
dc.subject.keywordPlus | Matrix algebra | - |
dc.subject.keywordPlus | Stochastic systems | - |
dc.subject.keywordPlus | Cold start problems | - |
dc.subject.keywordPlus | Conceptual frameworks | - |
dc.subject.keywordPlus | Cross-domain recommendations | - |
dc.subject.keywordPlus | Industrial scenarios | - |
dc.subject.keywordPlus | Matrix factorizations | - |
dc.subject.keywordPlus | Objective functions | - |
dc.subject.keywordPlus | Stochastic gradient descent algorithm | - |
dc.subject.keywordPlus | Stochastic gradient descent method | - |
dc.subject.keywordPlus | Recommender systems | - |
dc.subject.keywordPlus | algorithm | - |
dc.subject.keywordPlus | article | - |
dc.subject.keywordPlus | conceptual framework | - |
dc.subject.keywordPlus | filtration | - |
dc.subject.keywordPlus | loss of function mutation | - |
dc.subject.keywordPlus | prediction | - |
dc.subject.keywordPlus | stochastic model | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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