Enhance user experience and drive engagement with personalized recommendation systems. I specialize in developing advanced recommendation engines that deliver tailored suggestions to users, whether for e-commerce, streaming services, or any other application. By leveraging cutting-edge data science and machine learning techniques, I can create solutions that understand user preferences and predict their needs, boosting satisfaction and retention.
Services Offered:
- Development of collaborative filtering and content-based recommendation systems.
- Hybrid recommendation systems combining multiple techniques for improved accuracy.
- Personalized product, content, or service recommendations.
- Real-time recommendation engines for dynamic user interaction.
- Integration with existing platforms and databases.
- Analysis of user behavior and preferences to enhance recommendation accuracy.
- Continuous monitoring and updating of recommendation algorithms to maintain relevance.
- Custom reporting and visualization of recommendation performance metrics.
Skills and Tools:
- Data Analysis: Data cleaning, data preprocessing, exploratory data analysis (EDA)
- Machine Learning: Collaborative filtering, content-based filtering, matrix factorization, deep learning
- Algorithms: K-Nearest Neighbors (KNN), Singular Value Decomposition (SVD), Neural Collaborative Filtering (NCF)
- NLP for Recommendations: Word embeddings, semantic similarity
- Data Visualization: Matplotlib, Seaborn, Tableau, Power BI
- Programming Languages: Python, R
- Libraries and Frameworks: Scikit-learn, TensorFlow, PyTorch, Surprise
- Database Management: MySQL, PostgreSQL, MongoDB
- Version Control: Git, GitHub
- Project Management: Agile methodologies, JIRA, Trello