Related papers: A Survey of Latent Factor Models in Recommender Systems

A Survey of Latent Factor Models in Recommender Systems

URL: http://arxiv.org/abs/2405.18068v1
Date: Tue, 28 May 2024 11:28:59 GMT
Title: A Survey of Latent Factor Models in Recommender Systems
Authors: Hind I. Alshbanat, Hafida Benhidour, Said Kerrache,
Abstract summary: This survey systematically reviews latent factor models in recommender systems. The literature is examined through a structured framework covering learning data, model architecture, learning strategies, and optimization techniques.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

Related papers

A survey on the impact of AI-based recommenders on human behaviours: methodologies, outcomes and future directions [3.802956917145726]
This survey analyses the impact of recommenders in four human-AI ecosystems. Social media, online retail, urban mapping and generative AI ecosystems are studied.
arXiv Detail & Related papers (2024-06-29T14:34:32Z)
User Modeling and User Profiling: A Comprehensive Survey [0.0]
This paper presents a survey of the current state, evolution, and future directions of user modeling and profiling research. We provide a historical overview, tracing the development from early stereotype models to the latest deep learning techniques. We also address the critical need for privacy-preserving techniques and the push towards explainability and fairness in user modeling approaches.
arXiv Detail & Related papers (2024-02-15T02:06:06Z)
Training and Serving System of Foundation Models: A Comprehensive Survey [32.0115390377174]
This paper extensively explores the methods employed in training and serving foundation models from various perspectives. It provides a detailed categorization of these state-of-the-art methods, including finer aspects such as network, computing, and storage.
arXiv Detail & Related papers (2024-01-05T05:27:15Z)
Embedding in Recommender Systems: A Survey [67.67966158305603]
A crucial aspect is embedding techniques that covert the high-dimensional discrete features, such as user and item IDs, into low-dimensional continuous vectors. Applying embedding techniques captures complex entity relationships and has spurred substantial research. This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques.
arXiv Detail & Related papers (2023-10-28T06:31:06Z)
Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review [1.3630870408844922]
We conducted a systematic literature review to gather data on models typically employed in designing conversational recommender systems. We developed a decision model to assist researchers in selecting the most suitable models for their systems. Our study contributes practical insights and a comprehensive understanding of user intent modeling, empowering the development of more effective and personalized conversational recommender systems.
arXiv Detail & Related papers (2023-08-05T22:50:21Z)
A Survey on Large Language Models for Recommendation [77.91673633328148]
Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) This survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec)
arXiv Detail & Related papers (2023-05-31T13:51:26Z)
Deep networks for system identification: a Survey [56.34005280792013]
System identification learns mathematical descriptions of dynamic systems from input-output data. Main aim of the identified model is to predict new data from previous observations. We discuss architectures commonly adopted in the literature, like feedforward, convolutional, and recurrent networks.
arXiv Detail & Related papers (2023-01-30T12:38:31Z)
Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization [101.32332941117271]
Decision making algorithms are used in a multitude of different applications. Deep learning approaches that use highly parametric architectures tuned from data without relying on mathematical models are becoming increasingly popular. Model-based optimization and data-centric deep learning are often considered to be distinct disciplines.
arXiv Detail & Related papers (2022-05-05T13:40:08Z)
A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation [70.69134448863483]
Research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models.
arXiv Detail & Related papers (2021-04-27T08:03:52Z)
Model-Based Deep Learning [155.063817656602]
Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
arXiv Detail & Related papers (2020-12-15T16:29:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.