Real-time Machine Learning For Recommendations
eugeneyan.com
Real-time Machine Learning For Recommendations
In his April 2015 letter to shareholders, Amazon founder/CEO Jeff Bezos listed the four requirements for a “dreamy business”: “Customers love it, it can grow to very large size, it has strong returns on capital, and it’s durable in time—with the potential to endure for decades.” We want Prime to be such a good value, you'd be irresponsible not to b
... See moreAll of the effort spent deliberating on edge cases and long tails stems from the fact that many junior devs are not actually thinking hard enough about what the experiment should be, and what the metrics should look like.
The goal of building out these probabilistic software systems is not a milestone or a feature. Instead, what we're looking for a... See more
The goal of building out these probabilistic software systems is not a milestone or a feature. Instead, what we're looking for a... See more
Jason Liu • Tips for probabilistic software - jxnl.co
These gray-area business logic questions where recommender systems become just as much of an art as a science, continuing to generate potential content for recommendation, blending it, filtering out negative or prohibited content on the platform to get to a “good” feed of engageable content.
Vicki Boykis • What we talk about when we talk about The Algo
Hi everyone! How do you guys go about choosing the granularity of your ML response ?. For instance, let us say you have been tasked with predicting the purchase probability for an item and this is how your merch hierarchy looks -
1) department
2) category
3) sub category
4) item
The trade off here is between granularity and response sparsity ie if you ... See more
1) department
2) category
3) sub category
4) item
The trade off here is between granularity and response sparsity ie if you ... See more