Machine learning for personalized recommendation systems
Table Of Contents
<p>1. Introduction<br> 1.1 Background<br> 1.2 Motivation<br> 1.3 Objectives<br>2. Literature Review<br> 2.1 Overview of recommendation systems<br> 2.2 Types of recommendation algorithms<br> 2.3 Evaluation metrics for recommendation systems<br>3. Data Collection and Preprocessing<br> 3.1 Data sources<br> 3.2 Data cleaning and preprocessing<br>4. Feature Engineering<br> 4.1 User behavior analysis<br> 4.2 Item representation and feature extraction<br>5. Machine Learning Algorithms for Recommendations<br> 5.1 Collaborative filtering<br> 5.2 Content-based filtering<br> 5.3 Hybrid approaches<br></p>
Thesis Abstract
<p> This project aims to explore the application of machine learning algorithms in developing personalized recommendation systems. The project will focus on understanding user preferences and behavior to provide tailored recommendations for various types of content, such as movies, music, books, and products. The project will involve data collection, feature engineering, algorithm selection, and evaluation of the recommendation system's performance. <br></p>Thesis Overview
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