[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 …
Bias and debias in recommender system: A survey and future directions
While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …
system (RS), most of the papers focus on inventing machine learning models to better fit …
Artificial intelligence in recommender systems
Recommender systems provide personalized service support to users by learning their
previous behaviors and predicting their current preferences for particular products. Artificial …
previous behaviors and predicting their current preferences for particular products. Artificial …
DRN: A deep reinforcement learning framework for news recommendation
In this paper, we propose a novel Deep Reinforcement Learning framework for news
recommendation. Online personalized news recommendation is a highly challenging …
recommendation. Online personalized news recommendation is a highly challenging …
Estimation-action-reflection: Towards deep interaction between conversational and recommender systems
Recommender systems are embracing conversational technologies to obtain user
preferences dynamically, and to overcome inherent limitations of their static models. A …
preferences dynamically, and to overcome inherent limitations of their static models. A …
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 …
Interactive recommender system via knowledge graph-enhanced reinforcement learning
Interactive recommender system (IRS) has drawn huge attention because of its flexible
recommendation strategy and the consideration of optimal long-term user experiences. To …
recommendation strategy and the consideration of optimal long-term user experiences. To …
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 …
REDRL: A review-enhanced Deep Reinforcement Learning model for interactive recommendation
H Liu, K Cai, P Li, C Qian, P Zhao, X Wu - Expert Systems with Applications, 2023 - Elsevier
Recent advances in interactive recommender systems (IRS) have received wide attention
due to its flexible recommendation strategy and optimization for users' long-term utility …
due to its flexible recommendation strategy and optimization for users' long-term utility …