Elbio Renato Torres Abib

Improving quality of training data for learning to rank using click-through data

In information retrieval, relevance of documents with respect to queries is usually judged by humans, and used in evaluation and/or learning of ranking functions. Previous work has shown that certain level of noise in relevance judgments has little effect on evaluation, especially for comparison purposes. Recently learning to rank has become one of the major means to create ranking models in which the models are automatically learned from the data derived from a large number of relevance judgments. As far as we know, there was no previous work about quality of training data for learning to rank, and this paper tries to study the issue. Specifically, we address three problems. Firstly, we show that the quality of training data labeled by humans has critical impact on the performance of learning to rank algorithms. Secondly, we propose detecting relevance judgment errors using click-through data accumulated at a search engine. Two discriminative models, referred to as sequential dependency model and full dependency model, are proposed to make the detection. Both models consider the conditional dependency of relevance labels and thus are more powerful than the conditionally independent model previously proposed for other tasks. Finally, we verify that using training data in which the errors are detected and corrected by our method, we can improve the performance of learning to rank algorithms.

New heuristics and integer programming formulations for scheduling divisible load tasks

Divisible load applications occur in many fields of science and engineering. They can be parallelized in a master-worker fashion, but they pose several scheduling challenges. We propose single-round and multi-round integer programming formulations for scheduling divisible load applications with minimum makespan. An innovative linear-time exact algorithm improving the complexity of the best known algorithm to date is described for the special case in which the processor activation order is known beforehand. This algorithm is embedded within a greedy-with-feedback heuristic for finding good solutions for single-round problems. Numerical results illustrate the speed and the effectiveness of the proposed heuristic.