Application of Embedding Technology in Recommender Systems

Zhe Wang; Chao Pu; Felice Wang

doi:10.1017/9781009447515.006

4 - Application of Embedding Technology in Recommender Systems

Published online by Cambridge University Press: 08 May 2025

Zhe Wang ,

Chao Pu and

Felice Wang

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Summary

Embedding technology plays a pivotal role in deep learning, particularly in industries such as recommendation, advertising, and search. It is considered a fundamental operation for transforming sparse vectors into dense representations that can be further processed by neural networks. Beyond its basic role, embedding technology has evolved significantly in both academia and industry, with applications ranging from sequence processing to multifeature heterogeneous data. This chapter discusses the basics of embedding, its evolution from Word2Vec to graph embeddings and multifeature fusion, and its applications in recommender systems, with an emphasis on online deployment and inference.

Keywords

embedding Word2Vec Item2Vec graph embeddings dense retrieval locality sensitive hashing

Type: Chapter
Information: Deep Learning Recommender Systems , pp. 117 - 142

DOI: https://doi.org/10.1017/9781009447515.006 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2025

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