Our paper “A Unified Recommendation Model for Features Summarization” has been accepted at the 3rd @MuRS_WS, co-located with ACM #RecSys 2025 in Prague 🇨🇿!
🔗 Full paper here on Amazon Science: https://t.co/QpBpykS3bF
Happy to share that both our papers have been accepted to The Web Conference 2022! @AmazonScience#amazonscience
Modeling position bias ranking for streaming media services
https://t.co/CjqnivYYrj
Fair effect attribution in parallel online experiments
https://t.co/y4drADUgaR
Let's make our planet a little greener. Celebrate #EarthDay with me by saying, “Alexa, grow a tree,” to donate $1 to plant a tree with One Tree Planted.
@culturedcode Hey guys, best app ever for to-dos list. Just one thing is missing: sharing notes with other Things users. Plan to implement it any time soon? Cheers!
An excellent tutorial at #WSDM2021 on "Personalization in Practice". If you are working on personalization and recommender systems, this has lots of information on many aspects of #recsys. https://t.co/kcW8vIc6ON
The Levenberg-Marquardt is a standard method for non-linear least-squares, combining the best of gradient descent and Newton methods by replacing the Hessian by the Jacobian term only.
https://t.co/zQgk0X10XB
Can we view RL as supervised learning, but where we also "optimize" the data? New blog post by Ben, Aviral, and Abhishek: https://t.co/8wZp0pEiOx
The idea: modify (reweight, resample, etc.) the data so that supervised regression onto actions produces better policies. More below:
What's the largest learning rate for which SGD converges? In deterministic case with Hessian H it is 2/||H||, from basic linear algebra. For SGD, an equivalent rate is 2/Tr(H), derivation from Russo-Dye theorem: https://t.co/XEvZYD0LK0
@hermesDE Hallo, mein Paket wurde gestern nicht geliefert, weil Sie die falsche Adresse geschrieben haben! Meine Sendungsnummer lautet 70153172700335. Der korrekte Straßenname lautet Rückertstrasse! Bitte hilf mir!