Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162319
Title: User-specific recommender systems: from data to model
Authors: Lin, Zhuoyi
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Lin, Z. (2022). User-specific recommender systems: from data to model. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162319
Abstract: In 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.
URI: https://hdl.handle.net/10356/162319
DOI: 10.32657/10356/162319
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

Files in This Item:
File Description SizeFormat 
PhD_Thesis_final.pdf2.73 MBAdobe PDFView/Open

Page view(s)

45
Updated on Dec 5, 2022

Download(s) 50

19
Updated on Dec 5, 2022

Google ScholarTM

Check

Altmetric


Plumx

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