Development of a recommendation system for personalized video content delivery
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 Video Content Recommendation Systems<br> 2.2 Collaborative Filtering and Content-Based Filtering<br> 2.3 Deep Learning for Recommendation<br>3. Methodology<br> 3.1 Data Collection and Preprocessing<br> 3.2 User Behavior Analysis<br> 3.3 Collaborative Filtering Model<br> 3.4 Content-Based Filtering Model<br> 3.5 Deep Learning Model for Video Content Representation<br>4. System Design and Implementation<br> 4.1 User Interface and Interaction Design<br> 4.2 Backend Architecture and Data Storage<br> 4.3 Integration of Recommendation Models<br> 4.4 Real-time Personalization and Delivery<br>5. Evaluation and Performance Metrics<br> 5.1 User Engagement Metrics<br> 5.2 Content Discoverability Metrics<br> 5.3 Comparative Analysis with Baseline Models<br></p>
Thesis Abstract
<p> The rapid growth of online video streaming platforms has led to an overwhelming amount of video content available to users. As a result, personalized video content recommendation systems have become essential for enhancing user experience and engagement. In this project, we aim to develop a recommendation system for personalized video content delivery that leverages machine learning and user behavior analysis. The proposed system will utilize collaborative filtering, content-based filtering, and deep learning techniques to provide personalized video recommendations based on user preferences, viewing history, and contextual information. By tailoring video content delivery to individual user interests, the developed recommendation system seeks to improve user satisfaction, content discoverability, and platform engagement. <br></p>Thesis Overview
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