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 …
Causal inference in recommender systems: A survey and future directions
Recommender systems have become crucial in information filtering nowadays. Existing
recommender systems extract user preferences based on the correlation in data, such as …
recommender systems extract user preferences based on the correlation in data, such as …
A survey of large language models
Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …
On generative agents in recommendation
Recommender systems are the cornerstone of today's information dissemination, yet a
disconnect between offline metrics and online performance greatly hinders their …
disconnect between offline metrics and online performance greatly hinders their …
Conversational information seeking
Conversational information seeking (CIS) is concerned with a sequence of interactions
between one or more users and an information system. Interactions in CIS are primarily …
between one or more users and an information system. Interactions in CIS are primarily …
[HTML][HTML] Deep reinforcement learning in recommender systems: A survey and new perspectives
In light of the emergence of deep reinforcement learning (DRL) in recommender systems
research and several fruitful results in recent years, this survey aims to provide a timely and …
research and several fruitful results in recent years, this survey aims to provide a timely and …
Leveraging large language models in conversational recommender systems
A Conversational Recommender System (CRS) offers increased transparency and control to
users by enabling them to engage with the system through a real-time multi-turn dialogue …
users by enabling them to engage with the system through a real-time multi-turn dialogue …
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis
Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …
beginning to show some successes in real-world scenarios. However, much of the research …
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 …
[HTML][HTML] A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects
Recommender systems have significantly developed in recent years in parallel with the
witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) …
witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) …