A Survey of Latent Factor Models in Recommender Systems

TL;DR

This survey comprehensively reviews latent factor models in recommender systems, covering their principles, methodologies, recent advancements, and future research directions to guide researchers and practitioners.

Contribution

It provides a structured taxonomy and detailed analysis of latent factor models, including data types, model architectures, learning strategies, and optimization techniques, highlighting recent trends and gaps.

Findings

Taxonomy of latent factor models and learning data types

Analysis of diverse model architectures and learning strategies

Identification of research gaps and future directions

Abstract

Recommender systems are essential tools in the digital era, providing personalized content to users in areas like e-commerce, entertainment, and social media. Among the many approaches developed to create these systems, latent factor models have proven particularly effective. This survey systematically reviews latent factor models in recommender systems, focusing on their core principles, methodologies, and recent advancements. The literature is examined through a structured framework covering learning data, model architecture, learning strategies, and optimization techniques. The analysis includes a taxonomy of contributions and detailed discussions on the types of learning data used, such as implicit feedback, trust, and content data, various models such as probabilistic, nonlinear, and neural models, and an exploration of diverse learning strategies like online learning, transfer learning, and active learning. Furthermore, the survey addresses the optimization strategies used to train latent factor models, improving their performance and scalability. By identifying trends, gaps, and potential research directions, this survey aims to provide valuable insights for researchers and practitioners looking to advance the field of recommender systems.