Zobrazeno 1 - 10
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pro vyhledávání: '"Hunt, Jonathan"'
We propose a new class of deep reinforcement learning (RL) algorithms that model latent representations in hyperbolic space. Sequential decision-making requires reasoning about the possible future consequences of current behavior. Consequently, captu
Externí odkaz:
http://arxiv.org/abs/2210.01542
Offline reinforcement learning (RL), which aims to learn an optimal policy using a previously collected static dataset, is an important paradigm of RL. Standard RL methods often perform poorly in this regime due to the function approximation errors o
Externí odkaz:
http://arxiv.org/abs/2208.06193
Most recommender systems are myopic, that is they optimize based on the immediate response of the user. This may be misaligned with the true objective, such as creating long term user satisfaction. In this work we focus on mobile push notifications,
Externí odkaz:
http://arxiv.org/abs/2202.08812
Autor:
O'Brien, Conor, Thiagarajan, Arvind, Das, Sourav, Barreto, Rafael, Verma, Chetan, Hsu, Tim, Neufield, James, Hunt, Jonathan J
Online advertising has typically been more personalized than offline advertising, through the use of machine learning models and real-time auctions for ad targeting. One specific task, predicting the likelihood of conversion (i.e.\ the probability a
Externí odkaz:
http://arxiv.org/abs/2201.12666
Autor:
Yue, Yuguang, Xie, Yuanpu, Wu, Huasen, Jia, Haofeng, Zhai, Shaodan, Shi, Wenzhe, Hunt, Jonathan J
Listwise ranking losses have been widely studied in recommender systems. However, new paradigms of content consumption present new challenges for ranking methods. In this work we contribute an analysis of learning to rank for personalized mobile push
Externí odkaz:
http://arxiv.org/abs/2201.07681
Autor:
Belli, Luca, Tejani, Alykhan, Portman, Frank, Lung-Yut-Fong, Alexandre, Chamberlain, Ben, Xie, Yuanpu, Lum, Kristian, Hunt, Jonathan, Bronstein, Michael, Anelli, Vito Walter, Kalloori, Saikishore, Ferwerda, Bruce, Shi, Wenzhe
After the success the RecSys 2020 Challenge, we are describing a novel and bigger dataset that was released in conjunction with the ACM RecSys Challenge 2021. This year's dataset is not only bigger (~ 1B data points, a 5 fold increase), but for the f
Externí odkaz:
http://arxiv.org/abs/2109.08245
Industrial recommender systems are frequently tasked with approximating probabilities for multiple, often closely related, user actions. For example, predicting if a user will click on an advertisement and if they will then purchase the advertised pr
Externí odkaz:
http://arxiv.org/abs/2108.13475