Evaluating recommender systems: survey and framework
E Zangerle, C Bauer - ACM Computing Surveys, 2022 - dl.acm.org
The comprehensive evaluation of the performance of a recommender system is a complex
endeavor: many facets need to be considered in configuring an adequate and effective …
endeavor: many facets need to be considered in configuring an adequate and effective …
[HTML][HTML] A systematic review of value-aware recommender systems
Research on recommender systems (RSs) has traditionally focused on the design of
systems capable of suggesting items of interest for users. However, often the most important …
systems capable of suggesting items of interest for users. However, often the most important …
Recommendations with negative feedback via pairwise deep reinforcement learning
Recommender systems play a crucial role in mitigating the problem of information overload
by suggesting users' personalized items or services. The vast majority of traditional …
by suggesting users' personalized items or services. The vast majority of traditional …
Algorithmic effects on the diversity of consumption on spotify
A Anderson, L Maystre, I Anderson… - Proceedings of the web …, 2020 - dl.acm.org
On many online platforms, users can engage with millions of pieces of content, which they
discover either organically or through algorithmically-generated recommendations. While …
discover either organically or through algorithmically-generated recommendations. While …
Reinforcement learning to optimize long-term user engagement in recommender systems
Recommender systems play a crucial role in our daily lives. Feed streaming mechanism has
been widely used in the recommender system, especially on the mobile Apps. The feed …
been widely used in the recommender system, especially on the mobile Apps. The feed …
Multi-armed bandits in recommendation systems: A survey of the state-of-the-art and future directions
Abstract Recommender Systems (RSs) have assumed a crucial role in several digital
companies by directly affecting their key performance indicators. Nowadays, in this era of big …
companies by directly affecting their key performance indicators. Nowadays, in this era of big …
Neural interactive collaborative filtering
In this paper, we study collaborative filtering in an interactive setting, in which the
recommender agents iterate between making recommendations and updating the user …
recommender agents iterate between making recommendations and updating the user …
Surrogate for long-term user experience in recommender systems
Over the years we have seen recommender systems shifting focus from optimizing short-
term engagement toward improving long-term user experience on the platforms. While …
term engagement toward improving long-term user experience on the platforms. While …
Federated multi-armed bandits
Federated multi-armed bandits (FMAB) is a new bandit paradigm that parallels the federated
learning (FL) framework in supervised learning. It is inspired by practical applications in …
learning (FL) framework in supervised learning. It is inspired by practical applications in …
Embedding democratic values into social media AIs via societal objective functions
Mounting evidence indicates that the artificial intelligence (AI) systems that rank our social
media feeds bear nontrivial responsibility for amplifying partisan animosity: negative …
media feeds bear nontrivial responsibility for amplifying partisan animosity: negative …