E-commerce recommendation systems using collaborative filtering
Table Of Contents
- 1.Introduction
- 1.1Background and Motivation
- 1.2Objectives and Scope
- 2.Literature Review
- 2.1Overview of Recommendation Systems
- 2.2Collaborative Filtering Algorithms
- 3.Data Collection and Preprocessing
- 3.1Data Sources
- 3.2Data Cleaning and Transformation
- 4.Collaborative Filtering Implementation
- 4.1User-Item Collaborative Filtering
- 4.2Item-Item Collaborative Filtering
- 5.Evaluation Metrics
- 5.1Accuracy Metrics
- 5.2Diversity and Serendipity Metrics
Thesis Abstract
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.
Thesis Overview
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