E-commerce recommendation systems using collaborative filtering
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 Collaborative Filtering Algorithms<br>3. Data Collection and Preprocessing<br> 3.1 Data Sources<br> 3.2 Data Cleaning and Transformation<br>4. Collaborative Filtering Implementation<br> 4.1 User-Item Collaborative Filtering<br> 4.2 Item-Item Collaborative Filtering<br>5. Evaluation Metrics<br> 5.1 Accuracy Metrics<br> 5.2 Diversity and Serendipity Metrics<br></p>
Thesis Abstract
<p> E-commerce recommendation systems play a crucial role in enhancing user experience and driving sales. This project aims to develop a recommendation system for e-commerce platforms using collaborative filtering techniques. Collaborative filtering leverages user behavior and preferences to make personalized product recommendations. The system will be designed to analyze user interactions and product ratings to generate accurate and relevant recommendations, thereby improving customer satisfaction and engagement. <br></p>Thesis Overview
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