Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …

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 …

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 …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

The multi-armed bandit is an important framework for balancing exploration with exploitation
in recommendation. Exploitation recommends content (eg, products, movies, music playlists) …

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 …

In E-commerce platforms such as Amazon and TaoBao, ranking items in a search session is
a typical multi-step decision-making problem. Learning to rank (LTR) methods have been …

Text-based interactive recommendation provides richer user preferences and has
demonstrated advantages over traditional interactive recommender systems. However …

As information retrieval researchers, we not only develop algorithmic solutions to hard
problems, but we also insist on a proper, multifaceted evaluation of ideas. The literature on …

We study adversarial attacks that manipulate the reward signals to control the actions
chosen by a stochastic multi-armed bandit algorithm. We propose the first attack against two …