Comparison of Learning-To-Rank (LTR) models: Computational Aspects and Application to a Document Ranking Problem

In today's digital landscape, vast accessible resources have prompted the development of efficient Information Retrieval systems using machine learning, specifically through a discipline called Learning to Rank. This approach aims to order information sources for relevant query responses on abundant data, and can be applied beyond recommendation systems and search engines, e.g. to financial investments. This work presents a study on LTR, covering its theory, neural network applications, and practical implementation for document ranking using Python and MSLR-WEB10K dataset.

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