KuaiRec: A fully-observed dataset and insights for evaluating recommender systems
The progress of recommender systems is hampered mainly by evaluation as it requires real-
time interactions between humans and systems, which is too laborious and expensive. This …
time interactions between humans and systems, which is too laborious and expensive. This …
Causal embeddings for recommendation
Many current applications use recommendations in order to modify the natural user
behavior, such as to increase the number of sales or the time spent on a website. This …
behavior, such as to increase the number of sales or the time spent on a website. This …
Recogym: A reinforcement learning environment for the problem of product recommendation in online advertising
Recommender Systems are becoming ubiquitous in many settings and take many forms,
from product recommendation in e-commerce stores, to query suggestions in search …
from product recommendation in e-commerce stores, to query suggestions in search …
Generalization bounds and representation learning for estimation of potential outcomes and causal effects
Practitioners in diverse fields such as healthcare, economics and education are eager to
apply machine learning to improve decision making. The cost and impracticality of …
apply machine learning to improve decision making. The cost and impracticality of …
Open bandit dataset and pipeline: Towards realistic and reproducible off-policy evaluation
Off-policy evaluation (OPE) aims to estimate the performance of hypothetical policies using
data generated by a different policy. Because of its huge potential impact in practice, there …
data generated by a different policy. Because of its huge potential impact in practice, there …
Pessimistic reward models for off-policy learning in recommendation
O Jeunen, B Goethals - Proceedings of the 15th ACM Conference on …, 2021 - dl.acm.org
Methods for bandit learning from user interactions often require a model of the reward a
certain context-action pair will yield–for example, the probability of a click on a …
certain context-action pair will yield–for example, the probability of a click on a …
Improving ad click prediction by considering non-displayed events
Click-through rate (CTR) prediction is the core problem of building advertising systems. Most
existing state-of-the-art approaches model CTR prediction as binary classification problems …
existing state-of-the-art approaches model CTR prediction as binary classification problems …
Unbiased learning for the causal effect of recommendation
Increasing users' positive interactions, such as purchases or clicks, is an important objective
of recommender systems. Recommenders typically aim to select items that users will interact …
of recommender systems. Recommenders typically aim to select items that users will interact …
The music streaming sessions dataset
At the core of many important machine learning problems faced by online streaming
services is a need to model how users interact with the content they are served …
services is a need to model how users interact with the content they are served …
On the factory floor: ML engineering for industrial-scale ads recommendation models
R Anil, S Gadanho, D Huang, N Jacob, Z Li… - arXiv preprint arXiv …, 2022 - arxiv.org
For industrial-scale advertising systems, prediction of ad click-through rate (CTR) is a central
problem. Ad clicks constitute a significant class of user engagements and are often used as …
problem. Ad clicks constitute a significant class of user engagements and are often used as …