Half Transductive Ranking

Advances in Ranking

We study the standard retrieval task of ranking a fixed set of documents given a previously unseen query and pose it as the half-transductive ranking problem. The task is partly transductive as the document set is fixed. Existing transductive approaches are natural non-linear methods for this set, but have no direct out-ofsample extension. Functional approaches, on the other hand, can be applie...

Transductive Ranking on Graphs

In ranking, one is given examples of order relationships among objects, and the goal is to learn from these examples a real-valued ranking function that induces a ranking or ordering over the object space. We consider the problem of learning such a ranking function in a transductive, graph-based setting, where the object space is finite and is represented as a graph in which vertices correspond...

Transductive Ranking via Pairwise Regularized Least-Squares

A Latent Variable Ranking Model for Content-Based Retrieval

Since their introduction, ranking SVM models [11] have become a powerful tool for training content-based retrieval systems. All we need for training a model are retrieval examples in the form of triplet constraints, i.e. examples specifying that relative to some query, a database item a should be ranked higher than database item b. These types of constraints could be obtained from feedback of u...

Transductive Preference Learning using Self-training

In this paper, we investigate the problem of learning user preferences in the transductive setting. The key idea of our method is to conduct a semi-supervised learning in the selftraining framework by gradually labeling unlabeled data and repeatedly re-training using the most confidently classified instance pairs. An advantage of our method is that it is able to mine and utilize the data inform...