CCS Concepts: •  Computer science  $\rightarrow$  Recommender Systems

ACM Reference Format:

1 COLLABORATIVE FILTERING EMBEDDING
1.1 Matrix Factorization Scheme
1.2 Factorization Machines
1.3 Survey and Tool
1.4 Future Direction

2 SELF-SUPERVISED LEARNING FOR EMBEDDINGS
2.1 Contrastive Learning for Embeddings
2.2 Generative Self-supervised Learning Methods
2.3 Surveys and Tools
2.4 Future direction

3 GRAPH BASED EMBEDDING
3.1 Embedding of Homogeneous Graphs
3.2 Embedding of Bipartite Graphs
3.3 Embedding of Heterogeneous Graphs
3.4 Embedding of Hypergraphs
3.5 Surveys & Tools
3.6 Future Direction

4 HASH EMBEDDING
4.1 Single Function Hash Embedding
4.2 Multiple Functions Hash Embedding
4.3 DHE: Dense Hash Embeddings
4.4 Survey and Tool
4.5 Future Directions

5 AUTOML & EMBEDDING
5.1 Reinforcement learning based method
5.2 Gradient-based Method
5.3 Other methods
5.4 Survey and Tool
5.5 Future Direction

6 QUANTIZATION
6.1 Binary Quantization
6.2 Codebook Quantization
6.3 Online Quantization
6.4 Survey and Tool
6.5 Future Direction

7 APPLICATIONS OF EMBEDDING IN DEEP RECOMMENDATION SYSTEM
7.1 Embedding-based method for CTR tasks
7.2 Embedding-based method for retrieval tasks
7.3 Empowering Recommendation Systems with Enhanced Embeddings Using Large Language Models

8 CONCLUSION