Entity Aware Modelling: A Survey

TL;DR

This survey reviews recent machine learning approaches for entity-aware modeling, emphasizing methods to incorporate entity characteristics for personalized predictions, especially when such data is scarce, and discusses cross-disciplinary innovations enhancing these models.

Contribution

It organizes current literature on entity-aware modeling based on data availability and training data, highlighting recent interdisciplinary innovations improving personalization.

Findings

Entity-aware models improve personalized prediction accuracy.

Recent methods infer entity characteristics from data when not readily available.

Cross-disciplinary techniques like uncertainty quantification enhance modeling.

Abstract

Personalized prediction of responses for individual entities caused by external drivers is vital across many disciplines. Recent machine learning (ML) advances have led to new state-of-the-art response prediction models. Models built at a population level often lead to sub-optimal performance in many personalized prediction settings due to heterogeneity in data across entities (tasks). In personalized prediction, the goal is to incorporate inherent characteristics of different entities to improve prediction performance. In this survey, we focus on the recent developments in the ML community for such entity-aware modeling approaches. ML algorithms often modulate the network using these entity characteristics when they are readily available. However, these entity characteristics are not readily available in many real-world scenarios, and different ML methods have been proposed to infer these characteristics from the data. In this survey, we have organized the current literature on entity-aware modeling based on the availability of these characteristics as well as the amount of training data. We highlight how recent innovations in other disciplines, such as uncertainty quantification, fairness, and knowledge-guided machine learning, can improve entity-aware modeling.