234 points by mlmaster 1 year ago flag hide 20 comments
johnsmith 1 year ago next
Interesting project! I've been looking for a good way to implement personalized recommendations for an online learning platform I'm building. Will this work for smaller datasets?
gilfoyle 1 year ago next
Yes, the recommendation engine should work for smaller datasets as well. It utilizes a low-complexity matrix factorization algorithm to reduce computational requirements.
didyousaynet 1 year ago prev next
How does it handle cold start problem?
eternalhomer 1 year ago next
The recommendation engine incorporates hybrid methods such as a combination of collaborative filtering and content-based filtering, which helps mitigate the cold start problem.
learn_algo 1 year ago prev next
What kind of language/library did you use to build the recommendation engine?
im_groot 1 year ago next
The recommendation engine is built using Python and leverages widely used libraries such as NumPy, Pandas, Scikit-learn, and TensorFlow.
aiteacher 1 year ago prev next
How scalable is this solution? Did you think about integrating it with distributed computing frameworks?
datascirich 1 year ago next
This solution can be scaled further by implementing it on a distributed computing framework like Apache Spark, Dask, or leveraging cloud-based solutions.
neuralchick 1 year ago prev next
The demo is impressive. I'm curious if you used deep learning techniques in the project. Could you elaborate more on how you made it work?
deeplord 1 year ago next
The recommendation engine does implement deep learning techniques for advanced feature extraction and learning representations. The focus is on technologies such as autoencoders, embedding layers, and Siamese architectures.
runwithscissors 1 year ago prev next
What kind of evaluation metrics did you use for the recommendation engine? Could you share the results and any comparisons you made with other algorithms?
seriousstartup 1 year ago next
To evaluate the algorithm, we used multiple metrics including precision, recall, F1, and mean average precision (MAP). From comparisons with other algorithms, this solution has shown a significant increase in recommendation accuracy.
mathmagician 1 year ago prev next
Great to see the results. How do you plan on maintaining this project and adding new features in the future?
featurewhiz 1 year ago next
The plan is to release it as an open-source project and involve the community in maintaining and further developing the algorithm. This will enable us to add functionalities such as personalized explanations for recommendations, incorporating new ML techniques, and more.
brainybyte 1 year ago prev next
Do you have a link to the open-source code repository? I'm excited to see the implementation details.
frozensandwich 1 year ago next
The code is available at [github.com/USERNAME/RepositoryName](http://github.com/USERNAME/RepositoryName) under the MIT License. Check it out and feel free to contribute!
underestimated 1 year ago prev next
Awesome work! I would like to incorporate this recommendation engine into my website. What are the recommended steps for integrating it with my existing ML pipeline?
efficienteagle 1 year ago next
Integration can be streamlined by following the steps below: 1) Deploy the model as a RESTful API service using a library like Flask, 2) Connect your web application to the service via API calls, 3) Create an interface for sending user-specific data and receiving recommendations, 4) Implement caching mechanisms to minimize latency and minimize impact on your infrastructure.
fastfalcon 1 year ago prev next
Thanks for outlining the steps! Approx. how much latency should I expect from a single API call (considering reasonable hardware setup)?
turbotactics 1 year ago next
Considering an average hardware setup, the expected latency for a single API call would be in the order of tens to hundreds of milliseconds. Hardware upgrades could further reduce this value.