Scope
Developed novel recommendation algorithms and a Python library to handle dynamic user-item interactions in real-time streaming scenarios, addressing the cold-start problem and enabling efficient online learning.
Technology
- Algorithms: Factorization Machines, Sketching-based Online Learning, Relational Clustering
- Implementation: Python, NumPy, SciPy
- Use Cases: E-commerce recommendations, social networking, folksonomies
Key Innovations
- Incremental Factorization Machines: Persistently handles cold-starting items in online recommendation scenarios
- Dynamic Sketching: Efficiently captures and adapts to changing user-item interactions in streaming data
- FluRS Library: Python library providing flexible, efficient streaming recommendation algorithms with dependency injection design pattern
Publications & Presentations
- EuroSciPy 2017: FluRS: A Library for Streaming Recommendation Algorithms [Video] [Slides]
- CHIIR 2017: Sketching Dynamic User-Item Interactions for Online Item Recommendation [Paper] [Poster]
- RecProfile 2016: Incremental Factorization Machines for Persistently Cold-starting Online Item Recommendation [Paper] [Slides]
- WIMS 2015: User Modeling in Folksonomies: Relational Clustering and Tag Weighting [Paper] [Slides] [Code]
- Not exactly a "streaming" recommender, but it was a foundational study surfacing the limitations of batch algorithms.
Learn More
- Code: stream-recommender (Research implementations)
- Library: FluRS (Production-ready Python library)
- Article: FluRS: A Python Library for Online Item Recommendation
Author: Takuya Kitazawa
I am a product builder, mentor, and advocate for sustainable technology development with a decade of experience in AI/ML products, data systems, and digital transformation. Based in Canada and originally from Japan, I have lived and worked globally, including part-time residence in Malawi, Africa. Visit my portfolio to learn more about my work, or reach out to me at [email protected].