Click-through data has proven to be a critical resource for improving search ranking quality.
Though a large amount of click data can be easily collected by search engines, various …

Scientific literature with rich metadata can be represented as a labeled directed graph. This
graph representation enables a number of scientific tasks such as ad hoc retrieval or named …

We propose a manifold alignment based approach for heterogeneous domain adaptation. A
key aspect of this approach is to construct mappings to link different feature spaces in order …

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 …

Web search engines are increasingly deploying many features, combined using learning to
rank techniques. However, various practical questions remain concerning the manner in …

Learning to rank arises in many information retrieval applications, ranging from Web search
engine, online advertising to recommendation system. In learning to rank, the performance …

Many recommendation and retrieval tasks can be represented as proximity queries on a
labeled directed graph, with typed nodes representing documents, terms, and metadata, and …

Current learning to rank approaches commonly focus on learning the best possible ranking
function given a small fixed set of documents. This document set is often retrieved from the …

Several Web 2.0 applications allow users to assign keywords (or tags) to provide better
organization and description of the shared content. Tag recommendation methods may …

We propose a semi-supervised learning to rank algorithm. It learns from both labeled data
(pairwise preferences or absolute labels) and unlabeled data. The data can consist of …