Embedding in Recommender Systems: A Survey Article Swipe

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Xiangyu Zhao , Maolin Wang , Xinjian Zhao , Jiansheng Li , Shucheng Zhou , Dawei Yin , Qing Li , Jiliang Tang , Ruocheng Guo · YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2310.18608

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.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2310.18608

PDF

https://arxiv.org/pdf/2310.18608

OA Status

green

Cited By

6

Related Works

10

OpenAlex ID

https://openalex.org/W4388092643

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OpenAlex ID

https://openalex.org/W4388092643

Canonical identifier for this work in OpenAlex

DOI

https://doi.org/10.48550/arxiv.2310.18608

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Title

Embedding in Recommender Systems: A Survey

Work title

Type

preprint

OpenAlex work type

Language

en

Primary language

Publication year

2023

Year of publication

Publication date

2023-10-28

Full publication date if available

Authors

Xiangyu Zhao, Maolin Wang, Xinjian Zhao, Jiansheng Li, Shucheng Zhou, Dawei Yin, Qing Li, Jiliang Tang, Ruocheng Guo

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Landing page

https://arxiv.org/abs/2310.18608

Publisher landing page

PDF URL

https://arxiv.org/pdf/2310.18608

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Open access

Yes

Whether a free full text is available

OA status

green

Open access status per OpenAlex

OA URL

https://arxiv.org/pdf/2310.18608

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Concepts

Recommender system, Computer science, Embedding, Scalability, Leverage (statistics), Collaborative filtering, Exploit, Machine learning, Data science, Graph, Field (mathematics), Artificial intelligence, Theoretical computer science, Database, Computer security, Mathematics, Pure mathematics

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Cited by

6

Total citation count in OpenAlex

Citations by year (recent)

2025: 5, 2024: 1

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Related works (count)

10

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