Music prediction tasks range from predicting tags given a song or clip of audio, predicting the name of the artist, or predicting related songs given a song, clip, artist name or tag. That is, we are interested in every semantic relationship between the different musical concepts in our database. In realistically sized databases, the number of songs is measured in the hundreds of thousands or m...

Ranking is a central component in information retrieval systems; as such, many machine learning methods for building rankers have been developed in recent years. An open problem is transfer learning, i.e. how labeled training data from one domain/market can be used to build rankers for another. We propose a flexible transfer learning strategy based on sample selection. source domain training sa...

In many person re-identification applications, typically only a small number of labeled image pairs are available for training. To address this serious practical issue, we propose a novel semi-supervised ranking method which makes use of unlabeled data to improve the reidentification performance. It is shown that low density separation or graph propagation assumption is not valid under some con...

This paper is concerned with a new task of ranking, referred to as “supplementary data assisted ranking”, or “supplementary ranking” for short. Different from conventional ranking, in the new task, each query is associated with two sets of objects: the target objects that are to be ranked, and the supplementary objects whose orders are not of our interest. Existing learning to rank approaches (...

Data driven rank ordering refers to the rank ordering of new data items based on the ordering inherent in existing data items. This is a challenging problem, which has received increasing attention in recent years in the machine learning community. Its applications include product recommendation, information retrieval, financial portfolio construction, and robotics. It is common to construct or...

In this tutorial I will introduce ‘learning to rank’, a machine learning technology on constructing a model for ranking objects using training data. I will first explain the problem formulation of learning to rank, and relations between learning to rank and the other learning tasks. I will then describe learning to rank methods developed in recent years, including pointwise, pairwise, and listw...