Work

On the impact of overfitting in learning to rank using a margin loss: a case study in job recommender systems

Year: 2022

Type: conference-paper

Abstract: In learning to rank and recommender systems, it is typically untractable to directly optimize on metrics of interest such as recall or precision. Surrogate losses are instead used for learning: an important case are margin-based loss functions, which seek to separate relevant samples from irrelevant ones. This paper studies the relation between margin loss and the true metric in the real-world setting of job recommender systems.

Source: HAL

Authors: Nathan, Solal, Bied, Guillaume

Institutions: Université Paris-Saclay, Laboratoire Interdisciplinaire des Sciences du Numérique, Centre de Recherche en Économie et Statistique

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4387073571

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