Estudo comparativo de comitês de sub-redes neurais para o problema de aprender a ranquear
Nenhuma Miniatura disponível
Data
2023-09-01
Autores
Título da Revista
ISSN da Revista
Título de Volume
Editor
Universidade Federal de Goiás
Resumo
Learning to Rank (L2R) is a sub-area of Information Retrieval that aims to use machine
learning to optimize the positioning of the most relevant documents in the answer
ranking to a specific query. Until recently, the LambdaMART method, which corresponds
to an ensemble of regression trees, was considered state-of-the-art in L2R. However,
the introduction of AllRank, a deep learning method that incorporates self-attention
mechanisms, has overtaken LambdaMART as the most effective approach for L2R tasks.
This study, at issued, explored the effectiveness and efficiency of sub-networks ensemble
as a complementary method to an already excellent idea, which is the self-attention
used in AllRank, thus establishing a new level of innovation and effectiveness in the
field of ranking. Different methods for forming sub-networks ensemble, such as MultiSample Dropout, Multi-Sample Dropout (Training and Testing), BatchEnsemble and
Masksembles, were implemented and tested on two standard data collections: MSLRWEB10K and YAHOO!. The results of the experiments indicated that some of these
ensemble approaches, specifically Masksembles and BatchEnsemble, outperformed the
original AllRank in metrics such as NDCG@1, NDCG@5 and NDCG@10, although
they were more costly in terms of training and testing time. In conclusion, the research
reveals that the application of sub-networks ensemble in L2R models is a promising
strategy, especially in scenarios where latency time is not critical. Thus, this work not only
advances the state of the art in L2R, but also opens up new possibilities for improvements
in effectiveness and efficiency, inspiring future research into the use of sub-networks
ensemble in L2R.
Descrição
Citação
RIBEIRO, D. F. Estudo comparativo de comitês de sub-redes neurais para o problema de aprender a ranquear. 2023. 80 f. Dissertação (Mestrado em Ciência da Computação) - Instituto de Informática, Universidade Federal de Goiás, Goiânia, 2023.