Development of a recommendation system for personalized video content delivery
Table Of Contents
- <p>
- 1.Introduction<br>
- 1.1Background<br>
- 1.2Motivation<br>
- 1.3Objectives<br>
- 2.Literature Review<br>
- 2.1Video Content Recommendation Systems<br>
- 2.2Collaborative Filtering and Content-Based Filtering<br>
- 2.3Deep Learning for Recommendation<br>
- 3.Methodology<br>
- 3.1Data Collection and Preprocessing<br>
- 3.2User Behavior Analysis<br>
- 3.3Collaborative Filtering Model<br>
- 3.4Content-Based Filtering Model<br>
- 3.5Deep Learning Model for Video Content Representation<br>
- 4.System Design and Implementation<br>
- 4.1User Interface and Interaction Design<br>
- 4.2Backend Architecture and Data Storage<br>
- 4.3Integration of Recommendation Models<br>
- 4.4Real-time Personalization and Delivery<br>
- 5.Evaluation and Performance Metrics<br>
- 5.1User Engagement Metrics<br>
- 5.2Content Discoverability Metrics<br>
- 5.3Comparative Analysis with Baseline Models<br></p>
Project 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>Project Overview
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🎓 Postgraduate/Undergraduate Research works
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