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Table of Contents
I. Introduction
II. Definitions and Problem Description
III. Personalization Methods
A. Early shaping
B. Sample weighing
C. Transfer learning
D. Data grouping
IV. Evaluation
Summary
The document discusses training techniques, objectives, and metrics towards personalization of deep learning models. It focuses on the importance of personalization in various applications such as recommender systems and personal assistants. The methods presented include curriculum learning, data augmentation, and transfer learning. The goal is to tailor models to individual users while maintaining performance on a broader dataset. The document also addresses fairness and privacy concerns in personalization. Evaluation is done using the NIST Special Database 19, focusing on variation among individual data.