Recsperts - Recommender Systems Experts

#3: Bandits and Simulators for Recommenders with Olivier Jeunen

11 snips
Jan 3, 2022
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1
Introduction
00:00 • 3min
2
The Differences Between Advertising and Personalized Music
02:37 • 3min
3
Offline Approaches to Recommendation With Online Success
06:03 • 5min
4
The Problem With a Recommendation System
11:25 • 2min
5
How to Enable a Policy in a System
13:28 • 2min
6
The Trade-Off Between Exploration and Not Showing Good Recommendations
15:53 • 2min
7
How to Predict CTR Based on Offline Data
17:53 • 3min
8
The Benefits of Banded Learning for YouTube
20:55 • 2min
9
The Importance of Large Data in Advertising
22:41 • 3min
10
The Value of Rating Prediction in Online Recommendations
25:25 • 2min
11
The Benefits of a Simulated a-B Test
27:39 • 2min
12
How to Model Reinforcement Learning
29:45 • 3min
13
How to Measure User Enthusiasm
33:08 • 2min
14
Reward Engineering: An Open Problem for the Rexel Space
35:21 • 4min
15
How to Use Simulation Environments to Improve Banded Learning
38:53 • 2min
16
Building a Liquid-Gim Simulator
40:26 • 2min
17
How to Win a Data Science Challenge
42:12 • 4min
18
The Future of RACSIS Challenges
45:47 • 2min
19
The Difference Between Organic and Banded Feedback
48:01 • 2min
20
The Hard Statement: Is All Feedback Banded?
50:27 • 2min
21
The Future of Rekso
52:54 • 2min
22
Castle, Inference and Machine Learning
54:40 • 4min
23
The Importance of Learning
58:40 • 2min
24
The Greatest Challenges in the Rexxas Space
01:00:35 • 2min
25
How to Be a Successful PhD Student at Amazon
01:03:01 • 2min
26
How to Keep Growing in a Work Environment
01:04:48 • 2min
27
The Importance of Fairness in the Rexxo Space
01:06:59 • 2min
28
Rexxwords: Recommender Systems Expert
01:08:44 • 2min