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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 …
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 …
Collaborative filtering bandits
Classical collaborative filtering, and content-based filtering methods try to learn a static
recommendation model given training data. These approaches are far from ideal in highly …
recommendation model given training data. These approaches are far from ideal in highly …
Reinforcement learning for personalization: A systematic literature review
The major application areas of reinforcement learning (RL) have traditionally been game
playing and continuous control. In recent years, however, RL has been increasingly applied …
playing and continuous control. In recent years, however, RL has been increasingly applied …
Field study in deploying restless multi-armed bandits: Assisting non-profits in improving maternal and child health
The widespread availability of cell phones has enabled non-profits to deliver critical health
information to their beneficiaries in a timely manner. This paper describes our work to assist …
information to their beneficiaries in a timely manner. This paper describes our work to assist …
Federated linear contextual bandits
This paper presents a novel federated linear contextual bandits model, where individual
clients face different $ K $-armed stochastic bandits coupled through common global …
clients face different $ K $-armed stochastic bandits coupled through common global …
Dynamically expandable graph convolution for streaming recommendation
Personalized recommender systems have been widely studied and deployed to reduce
information overload and satisfy users' diverse needs. However, conventional …
information overload and satisfy users' diverse needs. However, conventional …
Coordinate Descent Method for -means
-means method using Lloyd heuristic is a traditional clustering method which has played a
key role in multiple downstream tasks of machine learning because of its simplicity …
key role in multiple downstream tasks of machine learning because of its simplicity …
Explaining the success of nearest neighbor methods in prediction
Many modern methods for prediction leverage nearest neighbor search to find past training
examples most similar to a test example, an idea that dates back in text to at least the 11th …
examples most similar to a test example, an idea that dates back in text to at least the 11th …