We propose a new learning to rank algorithm, named Weighted Margin-Rank Batch loss (WMRB), to extend the popular Weighted Approximate-Rank Pairwise loss (WARP). WMRB uses a new rank estimator and an efficient batch training algorithm. The approach allows more accurate item rank approximation and explicit utilization of parallel computation to accelerate training. In three item recommendation ta...

The Learning to Rank (L2R) research field has experienced a fast paced growth over the last few years, with a wide variety of benchmark datasets and baselines available for experimentation. We here investigate the main assumption behind this field, which is that, the use of sophisticated L2R algorithms and models, produce significant gains over more traditional and simple information retrieval ...

We study the problem of learning to rank from multiple sources. Though multi-view learning and learning to rank have been studied extensively leading to a wide range of applications, multi-view learning to rank as a synergy of both topics has received little attention. The aim of the paper is to propose a composite ranking method while keeping a close correlation with the individual rankings si...

We propose a new set of meta-level features to be used for learning how to combine classifier predictions with stacking. This set includes the probability distributions predicted by the base-level classifiers and a combination of these with the certainty of the predictions. We use these features in conjunction with multi-response linear regression (MLR) at the meta-level. We empirically evaluat...

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...

‘is paper presents an ecient application for driving large scale experiments on Learning to Rank (LtR) algorithms. We designed a so‰ware library that exploits caching mechanisms and ecient data structures to make the execution of massime experiments on LtR algorithms as fast as possible in order to try as many combinations of components as possible. ‘is presented so‰ware has been tested on di...

This paper presents our method to retrieve relevant queries given a new question in the context of Discovery Challenge: Learning to Re-Ranking Questions for Community Question Answering competition. In order to do that, a set of learning to rank methods was investigated to select an appropriate method. The selected method was optimized on training data by using a search strategy. After optimizi...

Traditional Learning to Rank models optimize a single ranking function for all available queries. Œis assumes that all queries come from a homogenous source. Instead, it seems reasonable to assume that queries originate from heterogenous sources, where certain queries may require documents to be ranked di‚erently. We introduce the Specialized Ranker Model which assigns queries to di‚erent ranke...

Water loss is a crucial factor for vegetation in the semi-arid Sahel region of Africa. Global satellite-driven estimates of plant CO2 uptake (gross primary productivity, GPP) have been found to not accurately account for Sahelian conditions, particularly the impact of canopy water stress. Here, we identify the main biophysical limitations that induce canopy water stress in Sahelian vegetation a...

This paper aims to combine learning-to-rank methods with an existing clustering underlying the entities to be ranked. In recent years, learning-to-rank has attracted the interest of many researchers and a large number of algorithmic approaches and methods have been published. Existing learning-to-rank methods have as goal to automatically construct a ranking model from training data. Usually, a...