Reinforcement learning based recommender systems: A survey
Recommender systems (RSs) have become an inseparable part of our everyday lives. They
help us find our favorite items to purchase, our friends on social networks, and our favorite …
help us find our favorite items to purchase, our friends on social networks, and our favorite …
[HTML][HTML] Advances and challenges in conversational recommender systems: A survey
Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …
heavily used in a wide range of industry applications. However, static recommendation …
CIRS: Bursting filter bubbles by counterfactual interactive recommender system
While personalization increases the utility of recommender systems, it also brings the issue
of filter bubbles. eg, if the system keeps exposing and recommending the items that the user …
of filter bubbles. eg, if the system keeps exposing and recommending the items that the user …
Hierarchical reinforcement learning for integrated recommendation
Integrated recommendation aims to jointly recommend heterogeneous items in the main
feed from different sources via multiple channels, which needs to capture user preferences …
feed from different sources via multiple channels, which needs to capture user preferences …
[PDF][PDF] SlateQ: A tractable decomposition for reinforcement learning with recommendation sets
E Ie, V Jain, J Wang, S Narvekar, R Agarwal, R Wu… - 2019 - cs.toronto.edu
Reinforcement learning (RL) methods for recommender systems optimize recommendations
for long-term user engagement. However, since users are often presented with slates of …
for long-term user engagement. However, since users are often presented with slates of …
Estimating and penalizing induced preference shifts in recommender systems
The content that a recommender system (RS) shows to users influences them. Therefore,
when choosing a recommender to deploy, one is implicitly also choosing to induce specific …
when choosing a recommender to deploy, one is implicitly also choosing to induce specific …
Preference dynamics under personalized recommendations
The design of content recommendation systems underpins many online platforms: social
media feeds, online news aggregators, and audio/video hosting websites all choose how …
media feeds, online news aggregators, and audio/video hosting websites all choose how …
Reinforcement learning applications
Y Li - arxiv preprint arxiv:1908.06973, 2019 - arxiv.org
We start with a brief introduction to reinforcement learning (RL), about its successful stories,
basics, an example, issues, the ICML 2019 Workshop on RL for Real Life, how to use it …
basics, an example, issues, the ICML 2019 Workshop on RL for Real Life, how to use it …
Do offline metrics predict online performance in recommender systems?
Recommender systems operate in an inherently dynamical setting. Past recommendations
influence future behavior, including which data points are observed and how user …
influence future behavior, including which data points are observed and how user …
Personalized approximate pareto-efficient recommendation
Real-world recommendation systems usually have different learning objectives and
evaluation criteria on accuracy, diversity or novelty. Therefore, multi-objective …
evaluation criteria on accuracy, diversity or novelty. Therefore, multi-objective …