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 …

Predicting the probability that a user will click on a specific advertisement has been a
prevalent issue in online advertising, attracting much research attention in the past decades …

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 …

Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking
on an ad or an item, is critical to many online applications such as online advertising and …

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 …

The fundamental mathematical tools needed to understand machine learning include linear
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …

Learning sophisticated feature interactions behind user behaviors is critical in maximizing
CTR for recommender systems. Despite great progress, existing methods seem to have a …

Predicting user responses, such as clicks and conversions, is of great importance and has
found its usage inmany Web applications including recommender systems, websearch and …

Clipper: A Low-Latency Online Prediction Serving System Page 1 This paper is included in the
Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation …

Advertising and feed ranking are essential to many Internet companies such as Facebook
and Sina Weibo. Among many real-world advertising and feed ranking systems, click …