Book contents
- Deep Learning Recommender Systems
- Reviews
- Deep Learning Recommender Systems
- Copyright page
- Contents
- Foreword
- Preface
- 1 Growth Engine of the Internet
- 2 Pre-Deep Learning Era
- 3 Top of the Tide
- 4 Application of Embedding Technology in Recommender Systems
- 5 Recommender Systems from Multiple Perspectives
- 6 Engineering Implementations in Deep Learning Recommender Systems
- 7 Evaluation in Recommender Systems
- 8 Frontier Practice of Deep Learning Recommender Systems
- 9 Build Your Own Recommender Systems Knowledge Framework
- Afterword
- References
4 - Application of Embedding Technology in Recommender Systems
Published online by Cambridge University Press: 08 May 2025
Book contents
- Deep Learning Recommender Systems
- Reviews
- Deep Learning Recommender Systems
- Copyright page
- Contents
- Foreword
- Preface
- 1 Growth Engine of the Internet
- 2 Pre-Deep Learning Era
- 3 Top of the Tide
- 4 Application of Embedding Technology in Recommender Systems
- 5 Recommender Systems from Multiple Perspectives
- 6 Engineering Implementations in Deep Learning Recommender Systems
- 7 Evaluation in Recommender Systems
- 8 Frontier Practice of Deep Learning Recommender Systems
- 9 Build Your Own Recommender Systems Knowledge Framework
- Afterword
- References
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.
Information
- Type
- Chapter
- Information
- Deep Learning Recommender Systems , pp. 117 - 142Publisher: Cambridge University PressPrint publication year: 2025
References
Mikolov, Tomas, et al. Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 26, 2013.Google Scholar
Mikolov, Tomas, et al. Efficient estimation of word representations in vector space: arXiv preprint arXiv:1301.3781 (2013).Google Scholar
Rong, Xin. Word2vec parameter learning explained: arXiv preprint arXiv:1411.2738 (2014).Google Scholar
Goldberg, Yoav, Levy, Omer. Word2vec explained: Deriving Mikolov et al.’s negative-sampling word-embedding method: arXiv preprint arXiv: 1402.3722 (2014).Google Scholar
Bengio, Yoshua, et al. A neural probabilistic language model. Journal of Machine Learning Research, 3, 2003: 1137–1155.Google Scholar
Barkan, Oren, Koenigstein, Noam. Item2vec: Neural item embedding for collaborative filtering. 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), Salerno, Italy, September 13–16, 2016.Google Scholar
Perozzi, Bryan, Al-Rfou, Rami, Skiena, Steven. DeepWalk: Online learning of social representations. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, USA, August 24–27, 2014.Google Scholar
Grover, Aditya, Leskovec, Jure. node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, August 13–17, 2016.Google Scholar
Wang, Jizhe, et al. Billion-scale commodity embedding for e-commerce recommender in Alibaba. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, UK, August 19–23, 2018.Google Scholar
Tang, Jian, et al. Line: Large-scale information network embedding. Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, Florence, Italy, May 18–22, 2015.Google Scholar
Wang, Daixin, Cui, Peng, Zhu, Wenwu. Structural deep network embedding. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, August 13–17, 2016.Google Scholar
Slaney, Malcolm, Casey, Michael. Locality-sensitive hashing for finding nearest neighbors [lecture notes]. IEEE Signal Processing Magazine, 25(2), 2008: 128–131.Google Scholar
Accessibility standard: Unknown
Why this information is here
This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.Accessibility Information
Accessibility compliance for the HTML of this chapter is currently unknown
and may be updated in the future.