70 points by crypticchaos 1 year ago flag hide 12 comments
john_doe 1 year ago next
Great post! I've been working on a recommendation engine with Rust and ML as well. I found using the Apriori algorithm for association rule learning to be really helpful in generating recommendations. Have you tried that out?
jane_doe 1 year ago next
Hey @john_doe, thanks for the suggestion! Yes, I have tried out the Apriori algorithm, and it worked quite well for my use case. The implementation in the `ar Rules` crate made it easy to use. I also used the `rocket` crate to build the server and the `lazy_static` crate to build the recommendation logic. Did you use similar or different libraries?
bob_builder 1 year ago prev next
Awesome work. Can you elaborate on how you used Rust and ML to build the recommendation engine and what your use case is?
helpfulturtle 1 year ago next
I'm also curious about the specifics of @jane_doe's use case. Would love to hear more details!
learner 1 year ago prev next
How long did it take you to learn Rust and ML concepts and build the recommendation engine? Were there any helpful resources you used for learning?
happy_learner 1 year ago next
I'm also interested in learning Rust and ML. Can you recommend any good resources for someone starting from scratch?
book_recommendations 1 year ago prev next
@learner, here are some resources that I found helpful: - The Rust Programming Language book ("The Book") - Rust by Example - Machine Learning Mastery blog - Jay Alammar's visual explanations of ML concepts ("Interpretable ML")
skeptical_mind 1 year ago prev next
Isn't Rust a bit of an unusual choice for building a recommendation engine? Why not use a more common language like Python or Java?
jane_doe 1 year ago next
I chose Rust for its performance and memory safety features. I found that Rust's performance was significantly better than Python's, and I didn't have to worry about memory leaks or data races. Additionally, the Rust ecosystem has been rapidly growing and has many great libraries for ML and web development.
future_project 1 year ago prev next
What do you plan to do next with your recommendation engine? How do you plan to scale and improve it?
jane_doe 1 year ago next
I plan to integrate my recommendation engine with more services and add personalization features. I also want to experiment with different ML algorithms and techniques to improve the accuracy of the recommendations. To scale it, I plan to use cloud infrastructure and distributed computing, such as Kubernetes and Apache Spark.
future_collaboration 1 year ago next
I'm working on a similar project and would love to collaborate on integrating our recommendation engines. Let me know if you're interested!