Development of a Recommendation System for Personalized Content Delivery
Table Of Contents
<p>1. Introduction<br> 1.1 Background and Motivation<br> 1.2 Objectives and Scope<br>2. Literature Review<br> 2.1 Overview of Recommendation Systems<br> 2.2 Analysis of Existing Personalized Content Delivery Platforms<br>3. Data Collection and Preprocessing<br> 3.1 Acquisition of User Interaction Data<br> 3.2 Feature Extraction and Representation<br>4. Recommendation Algorithms<br> 4.1 Collaborative Filtering Techniques<br> 4.2 Content-Based Filtering Approaches<br> 4.3 Hybrid Recommendation Strategies<br>5. System Implementation<br> 5.1 Selection of Machine Learning Frameworks<br> 5.2 Algorithm Development and Testing<br></p>
Thesis Abstract
<p> This project aims to develop a recommendation system that leverages machine learning algorithms to deliver personalized content to users based on their preferences, behavior, and historical interactions. The system will utilize collaborative filtering, content-based filtering, and hybrid recommendation techniques to provide accurate and relevant suggestions across various domains such as e-commerce, entertainment, and news. The project will focus on algorithmic efficiency, scalability, and user satisfaction to enhance the overall content delivery experience. <br></p>Thesis Overview
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