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 …

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

Implicit feedback (eg, clicks, dwell times, etc.) is an abundant source of data in human-
interactive systems. While implicit feedback has many advantages (eg, it is inexpensive to …

We develop a learning principle and an efficient algorithm for batch learning from logged
bandit feedback. This learning setting is ubiquitous in online systems (eg, ad placement …

With the rapid growth of web search in recent years the problem of modeling its users has
started to attract more and more attention of the information retrieval community. This has …

We develop a learning principle and an efficient algorithm for batch learning from logged
bandit feedback. This learning setting is ubiquitous in online systems (eg, ad placement …

Optimizing an interactive system against a predefined online metric is particularly
challenging, especially when the metric is computed from user feedback such as clicks and …

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 …

Click data collected by modern recommendation systems are an important source of
observational data that can be utilized to train learning-to-rank (LTR) systems. However …

In information retrieval, query auto completion (QAC), also known as typeahead [**ao et al.,
2013, Cai et al., 2014b] and auto-complete suggestion [Jain and Mishne, 2010], refers to the …