Name
Title | ||
|---|---|---|
REVEAL 2019: closing the loop with the real world: reinforcement and robust estimators for recommendation |
Abstract | ||
|---|---|---|
The REVEAL workshop1 focuses on framing the recommendation problem as a one of making personalized interventions. Moreover, these interventions sometimes depend on each other, where a stream of interactions occurs between the user and the system, and where each decision to recommend something will have an impact on future steps and long-term rewards. This framing creates a number of challenges we will discuss at the workshop. How can recommender systems be evaluated offline in such a context? How can we learn recommendation policies that are aware of these delayed consequences and outcomes?
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3298689.3346975 | Proceedings of the 13th ACM Conference on Recommender Systems |
Keywords | Field | DocType |
causal inference, multi-armed bandits, off-policy, offline evaluation, recommender systems, reinforcement learning | Computer science,Artificial intelligence,Reinforcement,Machine learning,Estimator | Conference |
ISBN | Citations | PageRank |
978-1-4503-6243-6 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thorsten Joachims | 1 | 17387 | 1254.06 |
| Maria Dimakopoulou | 2 | 1 | 0.70 |
| Adith Swaminathan | 3 | 229 | 12.68 |
| Yves Raimond | 4 | 373 | 45.93 |
| Olivier Koch | 5 | 0 | 1.01 |
| Flavian Vasile | 6 | 148 | 13.96 |