Posted here as well. www.linkedin.com/posts/gaurav...
I hope this gets the visibility to the the right audience
Posted here as well. www.linkedin.com/posts/gaurav...
I hope this gets the visibility to the the right audience
Also thanks to @recsysml.bsky.social for proofreading and providing me a platform to post. Follow them for more great recsys content.
A picture of the bluesky discover feed from the Apple App Store, showing a post by Jerry, and more importantly, a very cute corgi.
you and my dog are equally famous
If this post interests you come work with me!
jobs.gem.com/bluesky/am9i...
I made a post about personalization at Bluesky.
open.substack.com/pub/recsysml...
It’s impossible to watch any political thriller now because whatever incident is generating dramatic tension pales in comparison to reality.
Sign in PRO for presumed liability. app.leg.wa.gov/csi/House?se...
its on the roadmap!
A picture of house minority leader Hakeem Jeffries.
Every time I get in a Lyft they cal me Lan. Serifs could fix this very specific me problem.
If we want to create a more affordable, livable Seattle, we need to design for *reducing* car volumes. This is the only way to meet our climate, public health, vision zero, accessibility and housing goals. Our fight to design highway on-ramps with LESS capacity epitomizes why it's still hard.
In this three part series I compare #LLM and #RecommenderSystems to show the gaps and opportunities. They are surprisingly fewer than one would think.
Part 1 open.substack.com/pub/recsysml...
This project is absolutely nuts. You can take the vector embeddings for text and convert them back to the text that generated them.
github.com/vec2text/vec...
hah maybe. I feel like I would start with TransAct, and maybe PinSage to cover the world of recs at Pintrest
also a bunch of practical stuff on how they serve these long user histories. Clearly a lot of work went into this, and a lot of goodies in this paper.
- ranking model that tried to predict the next user action in a sequence. Use a cross-entropy loss to predict labels/actions on the sample and a next action loss to predict the next action the user will take.
- impression based negative sampling doubled some offline metrics
Read Xia et al., “TransAct V2.” earlier today, some notes:
- long and short term user histories (long term [10k items], short term [100], impressions [100])
- sampled soft max loss function seems useful in environments with very weak negatives
#recsys #ml
arxiv.org/abs/2506.02267
A corgi lying on the ground facing the camera. A red ball is sitting next to his snoot.
Atticus wants to play
but some more than others
you most of all
watching Alien: Earth and I think I'm rooting for the alien
todays discover outage was brought to you by the letter <SPACE>
anyone got opinions on data orchestration tools? Specifically Dagster vs Temporal?
This person has clearly never used protobufs, especially in python.
To celebrate Zohran Mamdani's apparent win in NYC I donated my democracy vouchers to my choice for mayor, Katie Wilson @wilsonforseattle.bsky.social . If you live in Seattle you can too! www.wilsonforseattle.com/democracy-vo...
Hah a friend and I managed to get something out of it. Though mostly Cronenberg isn’t ok. And I guess tech enables unhealthy obsessions
a test image
en.wikipedia.org/wiki/Main_Page
testing