  Recommender systems have become an essential component of many online
platforms, providing personalized recommendations to users. A crucial aspect is
embedding techniques that coverts the high-dimensional discrete features, such
as user and item IDs, into low-dimensional continuous vectors and can enhance
the recommendation performance. Applying embedding techniques captures complex
entity relationships and has spurred substantial research. In this survey, we
provide an overview of the recent literature on embedding techniques in
recommender systems. This survey covers embedding methods like collaborative
filtering, self-supervised learning, and graph-based techniques. Collaborative
filtering generates embeddings capturing user-item preferences, excelling in
sparse data. Self-supervised methods leverage contrastive or generative
learning for various tasks. Graph-based techniques like node2vec exploit
complex relationships in network-rich environments. Addressing the scalability
challenges inherent to embedding methods, our survey delves into innovative
directions within the field of recommendation systems. These directions aim to
enhance performance and reduce computational complexity, paving the way for
improved recommender systems. Among these innovative approaches, we will
introduce Auto Machine Learning (AutoML), hash techniques, and quantization
techniques in this survey. We discuss various architectures and techniques and
highlight the challenges and future directions in these aspects. This survey
aims to provide a comprehensive overview of the state-of-the-art in this
rapidly evolving field and serve as a useful resource for researchers and
practitioners working in the area of recommender systems.
