User-specific recommender systems: from data to model

• Main Author: Lin, Zhuoyi
• Other Authors: Kwoh Chee Keong
• Format: Thesis-Doctor of Philosophy
• Language: English
• Published: Nanyang Technological University 2022
• Subjects: Engineering::Computer science and engineering
• Online Access: https://hdl.handle.net/10356/162319
• Institution: Nanyang Technological University

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.