Steffen Rendle, Research Scientist, Google @ MLconf SF

(35:9)

Session 8: Revisiting the Performance of iALS on Item Recommendation Benchmarks

(16:56)

Robust Large-Scale Machine Learning in the Cloud

(25:2)

Software Engineering ​: Greatest Hits 1947 2047 Mark Rendle \u0026 Dylan Beattie

(52:17)

KDD2016 paper 801

(2:32)

RecSys 2020 Session P7B: Understanding and Modeling Preferences

(1:32:51)

RecSys 2020 Session P7A: Understanding and Modeling Preferences

(1:33:19)

RecSys 2020 Tutorial: Feature Engineering for Recommender Systems

(2:11:)

Matrix Factorization for Movie Recommendations – CME 510

(1:1econd)

47th #ebaytechtalk: Deep Learning for Recommender Systems

(1:4:12)

Small Steps, Giant Leaps: Engineering Lessons from Apollo - Dylan Beattie, Kevlin Henney

(1:7:30)

Deep Learning for Recommender Systems (Nick Pentreath)

(31:54)

Lecture 55 — Latent Factor Recommender System | Stanford University

(14:17)

Futurology for Dummies - the Next 30 Years in Tech - Mark Rendle

(1:4:13)

Doing I.T. for SCIENCE! - Sprints, Startups and the Scientific Method - Dylan Beattie

(46:56)

In a Monastery Garden - Ronnie Ronalde 1953

(3:16)

Xavier Amatriain Lecture 3+4

(1:54:44)

AdKDD 2024: Training Differentially Private Ad Prediction Models With Semi-Sensitive Features

(10:)

Session 8: You Say Factorization Machine, I Say Neural Network It’s All in the Activation

(13:47)

Master the Craft of B2B Marketing and Sales Analysis with Steffen Hedebrandt

(33:36)

xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

(2:57)

Optimizing Deep Learning Recommender Systems On CPU Architectures

(5:6)

FAKE + Paket – PowerTools for .NET developers - Steffen Forkmann

(1:4:12)