[Paper Review] Recommendation with Generative Models

Yashar Deldjoo, Zhankui He+9 more|arXiv (Cornell University)|Sep 18, 2024

Recommender Systems and Techniques4 citations

TL;DR

This book presents a comprehensive framework for generative recommender systems (Gen-RecSys), introducing a novel taxonomy that classifies deep generative models (DGMs) into ID-driven models, large language models (LLMs), and multimodal models. It demonstrates how these models enhance recommendation accuracy, diversity, and personalization by generating structured data, text interactions, and multimedia content, with a focus on robust evaluation and risk mitigation in real-world applications like e-commerce and media.

ABSTRACT

Generative models are a class of AI models capable of creating new instances of data by learning and sampling from their statistical distributions. In recent years, these models have gained prominence in machine learning due to the development of approaches such as generative adversarial networks (GANs), variational autoencoders (VAEs), and transformer-based architectures such as GPT. These models have applications across various domains, such as image generation, text synthesis, and music composition. In recommender systems, generative models, referred to as Gen-RecSys, improve the accuracy and diversity of recommendations by generating structured outputs, text-based interactions, and multimedia content. By leveraging these capabilities, Gen-RecSys can produce more personalized, engaging, and dynamic user experiences, expanding the role of AI in eCommerce, media, and beyond. Our book goes beyond existing literature by offering a comprehensive understanding of generative models and their applications, with a special focus on deep generative models (DGMs) and their classification. We introduce a taxonomy that categorizes DGMs into three types: ID-driven models, large language models (LLMs), and multimodal models. Each category addresses unique technical and architectural advancements within its respective research area. This taxonomy allows researchers to easily navigate developments in Gen-RecSys across domains such as conversational AI and multimodal content generation. Additionally, we examine the impact and potential risks of generative models, emphasizing the importance of robust evaluation frameworks.

Motivation & Objective

• To develop a unified taxonomy for deep generative models (DGMs) in recommendation systems.
• To address the limitations of traditional recommenders by enabling dynamic, personalized, and diverse recommendations through generative capabilities.
• To provide a comprehensive analysis of technical advancements across ID-driven models, LLMs, and multimodal models.
• To examine the risks and evaluation challenges associated with generative models in real-world recommendation deployment.
• To expand on prior work with new chapters, context, and in-depth analysis, establishing a foundational reference for Gen-RecSys research.

Proposed method

• Proposes a three-tier taxonomy of DGMs: ID-driven models, large language models (LLMs), and multimodal models, based on architectural and functional distinctions.
• Integrates generative modeling techniques such as variational autoencoders (VAEs), generative adversarial networks (GANs), and transformer-based architectures (e.g., GPT) into recommendation pipelines.
• Leverages text-based interaction generation and multimedia content synthesis to enrich user-item representations and improve recommendation quality.
• Employs structured data generation to model user preferences beyond implicit feedback, enabling more nuanced and personalized recommendations.
• Emphasizes the need for robust evaluation frameworks to assess fairness, accuracy, and robustness in Gen-RecSys applications.
• Builds on two prior arXiv submissions (arXiv:2409.10993v1 and arXiv:2408.10946v1) with expanded content, analysis, and new chapters to form a complete book-length work.

Experimental results

Research questions

• RQ1 How can deep generative models be systematically categorized within the context of recommender systems?
• RQ2 What are the distinct technical and architectural advancements that differentiate ID-driven models, LLMs, and multimodal models in recommendation?
• RQ3 How do generative models improve recommendation accuracy, diversity, and personalization compared to traditional methods?
• RQ4 What are the key risks and challenges in deploying generative models in production recommender systems?
• RQ5 How can evaluation frameworks be designed to ensure robustness, fairness, and reliability in Gen-RecSys applications?

Key findings

• The proposed taxonomy effectively organizes DGMs into three distinct categories—ID-driven models, LLMs, and multimodal models—enabling clearer navigation of research advancements.
• Generative models significantly enhance recommendation quality by generating structured outputs, text interactions, and multimedia content, leading to more engaging and personalized user experiences.
• LLMs and multimodal models demonstrate strong capabilities in generating contextually relevant and diverse recommendations, especially in conversational and content-rich environments.
• The integration of generative modeling into recommender systems enables dynamic adaptation to user preferences and evolving user behavior.
• Robust evaluation frameworks are essential to mitigate risks such as bias, hallucination, and distribution shift in real-world Gen-RecSys deployments.
• The book provides a comprehensive, expanded reference that consolidates and extends prior research, offering a foundational resource for future work in the field.