Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162319
Full metadata record
DC FieldValueLanguage
dc.contributor.authorLin, Zhuoyien_US
dc.date.accessioned2022-10-14T00:51:42Z-
dc.date.available2022-10-14T00:51:42Z-
dc.date.issued2022-
dc.identifier.citationLin, Z. (2022). User-specific recommender systems: from data to model. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162319en_US
dc.identifier.urihttps://hdl.handle.net/10356/162319-
dc.description.abstractIn the era of big data, recommender systems are widely adopted by online platforms (e.g., Amazon and YouTube), so as to provide target users with meaningful recommendation and alleviate the problem of information overload. To achieve personalized recommendation, an ideal solution would be to assign each user a prediction model. However, such a solution is impractical, which would lead to inefficiency problems, potential training issues, and low generalization. Therefore, tailored solutions need to be designed in order to achieve both effectiveness and efficiency. In this doctoral thesis, we present our previous works about exploiting user-specific recommendation approaches for personalized recommendation.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleUser-specific recommender systems: from data to modelen_US
dc.typeThesis-Doctor of Philosophyen_US
dc.contributor.supervisorKwoh Chee Keongen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeDoctor of Philosophyen_US
dc.contributor.supervisor2Xu Chien_US
dc.identifier.doi10.32657/10356/162319-
dc.contributor.supervisoremailASCKKWOH@ntu.edu.sgen_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:SCSE Theses
Files in This Item:
File Description SizeFormat 
PhD_Thesis_final.pdf2.73 MBAdobe PDFThumbnail
View/Open

Page view(s)

237
Updated on Apr 17, 2024

Download(s) 50

127
Updated on Apr 17, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.