A relevant and timely recommendation can be a pleasant surprise that will delight your users. Unfortunately it can be difficult to build a system that will produce useful suggestions, which is why this week’s guest, Nicolas Hug, built a library to help with developing and testing collaborative recommendation algorithms. He explains how he took the code he wrote for his PhD thesis and cleaned it up to release as an open source library and his plans for future development on it.
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- Your host as usual is Tobias Macey and today I’m interviewing Nicolas Hug about Surprise, a scikit library for building recommender systems
- How did you get introduced to Python?
- What is Surprise and what was your motivation for creating it?
- What are the most challenging aspects of building a recommender system and how does Surprise help simplify that process?
- What are some of the ways that a user or company can bootstrap a recommender system while they accrue data to use a collaborative algorithm?
- What are some of the ways that a recommender system can be used, outside of the typical ecommerce example?
- Once an algorithm has been deployed how can a user test the accuracy of the suggestions?
- How is Surprise implemented and how has it evolved since you first started working on it?
- What have been the most difficult aspects of building and maintaining Surprise?
- What are the attributes of the system that can be modified to improve the relevance of the recommendations that are provided?
- For someone who wants to use Surprise in their application, what are the steps involved?
- What are some of the new features or improvements that you have planned for the future of Surprise?
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