Embedding in Recommender Systems: A Survey

TL;DR

This survey reviews recent advances in embedding techniques for recommender systems, covering matrix, sequential, and graph-based methods, and discusses scalability, efficiency, and future directions including LLMs.

Contribution

It provides a comprehensive overview of state-of-the-art embedding methods in recommender systems, highlighting emerging approaches and future research challenges.

Findings

Collaborative filtering effectively models preferences in sparse data.

Sequential models include RNNs and self-supervised learning approaches.

Graph-based methods like node2vec leverage network relationships.

Abstract

Recommender systems have become an essential component of many online platforms, providing personalized recommendations to users. A crucial aspect is embedding techniques that convert the high-dimensional discrete features, such as user and item IDs, into low-dimensional continuous vectors, which can enhance the recommendation performance. Embedding techniques have revolutionized the capture of complex entity relationships, generating significant research interest. This survey presents a comprehensive analysis of recent advances in recommender system embedding techniques. We examine centralized embedding approaches across matrix, sequential, and graph structures. In matrix-based scenarios, collaborative filtering generates embeddings that effectively model user-item preferences, particularly in sparse data environments. For sequential data, we explore various approaches including recurrent neural networks and self-supervised methods such as contrastive and generative learning. In graph-structured contexts, we analyze techniques like node2vec that leverage network relationships, along with applicable self-supervised methods. Our survey addresses critical scalability challenges in embedding methods and explores innovative directions in recommender systems. We introduce emerging approaches, including AutoML, hashing techniques, and quantization methods, to enhance performance while reducing computational complexity. Additionally, we examine the promising role of Large Language Models (LLMs) in embedding enhancement. Through detailed discussion of various architectures and methodologies, this survey aims to provide a thorough overview of state-of-the-art embedding techniques in recommender systems, while highlighting key challenges and future research directions.