Problem

Recommender systems are vital for online platforms, but they face challenges in effectively processing high-dimensional, discrete features like user and item IDs. Traditional methods struggle to capture the intricate relationships between entities and to manage the immense scale of modern datasets. This leads to issues such as sparse data, cold-start problems, and computational inefficiencies, which embedding techniques aim to address by transforming these features into low-dimensional continuous vectors.

Method

This survey categorizes embedding techniques into several key areas. Centralized embedding approaches include matrix-based methods like Collaborative Filtering (Matrix Factorization, Factorization Machines, and their deep learning extensions), which excel in modeling user-item preferences, especially in sparse environments. For sequential data, techniques range from Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) to Transformer-based models, often enhanced by self-supervised learning (contrastive and generative methods) to capture temporal dependencies and high-order patterns. Graph-structured contexts utilize Graph Neural Networks (GNNs)—both spectral and spatial—graph pooling, and graph self-supervised learning (contrastive, generative, and predictive) across homogeneous, bipartite, heterogeneous, and hypergraph structures to leverage topological information. To address scalability, the survey covers AutoML for optimizing embedding sizes, hashing techniques (single, multi-function, and dense) to reduce dimensionality and computational overhead, and quantization methods (binary and codebook-based) to compress embeddings. Emerging approaches also include the use of Large Language Models (LLMs) to provide semantic enrichment and guidance for embeddings, either through direct inference or fine-tuning, and by offering self-guidance or cross-embedding guidance.

Results

The surveyed embedding techniques have significantly enhanced recommender system performance by effectively capturing complex user-item relationships. Collaborative filtering methods, particularly matrix factorization and factorization machines, have proven adept at handling sparse data and alleviating cold-start issues by learning latent preferences. Sequential embedding models, including those based on RNNs, CNNs, and Transformers, alongside self-supervised learning, successfully model temporal dynamics and higher-order patterns in user interactions. Graph-based embeddings, leveraging GNNs, have shown strong capabilities in exploiting rich structural information within various graph types (homogeneous, bipartite, heterogeneous, hypergraphs) for improved recommendations. Furthermore, techniques like AutoML, hashing, and quantization have demonstrated success in addressing computational complexity and scalability challenges, making embedding-based systems more efficient for large-scale deployment. The integration of Large Language Models has also begun to show promise in enriching embeddings with semantic understanding, leading to more nuanced and context-aware recommendations.

Implications

While embedding techniques have revolutionized recommender systems, several critical challenges and future directions remain. Scalability continues to be a major concern, particularly with the increasing size of datasets and the computational demands of advanced models like LLMs. Future research needs to focus on more efficient deployment strategies, including optimized caching and lightweight architectures. Enhanced interpretability of learned embeddings is crucial for building trust and understanding model decisions. Addressing fairness and bias in embedding representations is also paramount, as models can inadvertently amplify societal biases present in training data, necessitating fairness-aware development and evaluation. Additionally, further exploration into leveraging topological insights from graph theory and developing multi-task or dynamic quantization approaches can lead to more robust and adaptive recommendation systems. The ongoing integration of LLMs offers significant potential for semantic enrichment and knowledge transfer, but requires careful consideration of computational efficiency, privacy, and bias mitigation for practical, trustworthy real-world applications.