Recommendation System for E-commerce Using Collaborative Filtering

Introduction

The rapid growth of e-commerce has led to an increased need for personalized shopping experiences. Recommendation systems play a crucial role in enhancing user satisfaction by suggesting products that align with individual preferences. This project proposal outlines the development of a recommendation system for e-commerce platforms using collaborative filtering techniques.

Background

Collaborative filtering is one of the most widely used techniques in recommendation systems. It leverages user-item interactions to predict user preferences and suggest items that similar users have liked. Recent research has demonstrated the effectiveness of collaborative filtering in improving recommendation accuracy and user engagement.

Project Objective

The primary objective of this project is to develop an efficient recommendation system that utilizes collaborative filtering to enhance the shopping experience on e-commerce platforms. The system aims to provide personalized product recommendations by analyzing user behavior and preferences.

Methodology

1. Data Collection and Preprocessing

• Datasets: Utilize publicly available datasets such as the MovieLens dataset or Amazon product reviews dataset for training and evaluation.
• Data Cleaning: Preprocess the data to handle missing values, remove duplicates, and normalize user-item interactions.

2. Model Development

• Collaborative Filtering Techniques: Implement both user-based and item-based collaborative filtering methods.
• Matrix Factorization: Use matrix factorization techniques like Singular Value Decomposition (SVD) to improve recommendation accuracy.

3. Training and Evaluation

• Training: Train the model using historical user-item interaction data.
• Evaluation Metrics: Measure performance using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and precision-recall.

Expected Outcomes

The proposed system is expected to deliver highly accurate product recommendations, thereby improving customer satisfaction and increasing sales on e-commerce platforms. By leveraging collaborative filtering techniques, the system should effectively capture user preferences and adapt to changing trends.

Conclusion

This project aims to advance the field of e-commerce recommendation systems by developing a robust solution that enhances personalization through collaborative filtering. The integration of advanced techniques like matrix factorization is anticipated to provide significant improvements in recommendation accuracy.

For further details on related research, please refer to the paper "Recommendation System for E-commerce Using Collaborative Filtering," available at https://ieeexplore.ieee.org/document/8776589.

For dataset access, consider using resources like the MovieLens dataset or Amazon product reviews dataset.