The goal of the Redundancy, Diversity, and Interdependent Document Relevance workshop
was to explore how ranking, performance assessment and learning to rank can move …

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that
arise in quantum physics and random matrix theory. In contrast to traditional structured …

Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking
model using training data, such that the model can sort new objects according to their …

This chapter gives a general introduction to learning for ranking aggregation. Ranking
aggregation is aimed at combining multiple rankings into a single ranking, which is better …

Digital storage of personal music collections and cloud-based music services (eg Pandora,
Spotify) have fundamentally changed how music is consumed. In particular, automatically …

Determinantal point processes (DPPs) have recently been proposed as models for set
selection problems where diversity is preferred. For example, they can be used to select …

We present an on-line learning framework tailored towards real-time learning from observed
user behavior in search engines and other information retrieval systems. In particular, we …

Ranking in information retrieval has been traditionally approached as a pursuit of relevant
information, under the assumption that the users' information needs are unambiguously …

Much effort has been directed at algorithms for obtaining the highest probability (MAP)
configuration in probabilistic (random field) models. In many situations, one could benefit …

In this study, we investigate diversified recommendation problem by supervised learning,
seeking significant improvement in diversity while maintaining accuracy. In particular, we …