Thompson sampling is an algorithm for online decision problems where actions are taken
sequentially in a manner that must balance between exploiting what is known to maximize …

In recent years, the textile and fashion industries have witnessed an enormous amount of
growth in fast fashion. On e-commerce platforms, where numerous choices are available, an …

Abstract Out-of-distribution (OOD) detection is indispensable for machine learning models
deployed in the open world. Recently, the use of an auxiliary outlier dataset during training …

Thompson Sampling (TS) is one of the most effective algorithms for solving contextual multi-
armed bandit problems. In this paper, we propose a new algorithm, called Neural Thompson …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

In this paper, we propose a novel Deep Reinforcement Learning framework for news
recommendation. Online personalized news recommendation is a highly challenging …

People often ask others for restaurant recommendations as a way to discover new dining
experiences. This makes restaurant recommendation an exciting scenario for recommender …

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 …

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 …

Recommender systems aim to maximize the overall accuracy for long-term
recommendations. However, most of the existing recommendation models adopt a static …