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dc.contributor.authorLin, Zhuoyien_US
dc.identifier.citationLin, Z. (2022). User-specific recommender systems: from data to model. Doctoral thesis, Nanyang Technological University, Singapore.
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.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
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