mlsanityData to 7 Oct 2026

Neural ranking models for document retrieval

arXiv 2102.11903 ↗First posted 23 Feb 2021cs.IR cs.LGcited by 16

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Abstract (arXiv)

Ranking models are the main components of information retrieval systems. Several approaches to ranking are based on traditional machine learning algorithms using a set of hand-crafted features. Recently, researchers have leveraged deep learning models in information retrieval. These models are trained end-to-end to extract features from the raw data for ranking tasks, so that they overcome the limitations of hand-crafted features. A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking. In this paper, we compare the proposed models in the literature along different dimensions in order to understand the major contributions and limitations of each model. In our discussion of the literature, we analyze the promising neural components, and propose future research directions. We also show the analogy between document retrieval and other retrieval tasks where the items to be ranked are structured documents, answers, images and videos.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 91 of its 196 references that are in the map. First posted on arXiv 23 Feb 2021.

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91 of 196 references.

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Citing papers per year: 2021: 2; 2022: 1; 2023: 5; 2024: 3; 2025: 3; 2026: 2.

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