Self-residual Embedding for Click-Through Rate Prediction

Jingqin SUN, Yunfei YIN*, Faliang HUANG, Mingliang ZHOU, Leong Hou U

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

1 Citation (Scopus)


In the Internet, categorical features are high-dimensional and sparse, and to obtain its low-dimensional and dense representation, the embedding mechanism plays an important role in the click-through rate prediction of the recommendation system. Prior works have proved that residual network is helpful to improve the performance of deep learning models, but there are few works to learn and optimize the embedded representation of raw features through residual thought in recommendation systems. Therefore, we designed a self-residual embedding structure to learn the distinction between the randomly initialized embedding vector and the ideal embedding vector by calculating the self-correlation score, and applied it to our proposed SRFM model. Extensive experiments on four real datasets show that the SRFM model can achieve satisfactory performance compared with the superior model. Also, the self-residual embedding mechanism can improve the prediction performance of some existing deep learning models to a certain extent.

Original languageEnglish
Title of host publicationWeb and Big Data : 5th International Joint Conference, APWeb-WAIM 2021, Proceedings
EditorsLeong Hou U, Marc SPANIOL, Yasushi SAKURAI, Junying CHEN
PublisherSpringer, Cham
Number of pages15
ISBN (Print)9783030858988
Publication statusPublished - 2021
Externally publishedYes
Event5th International Joint Conference on Asia-Pacific Web and Web-Age Information Management, APWeb-WAIM 2021 - Guangzhou, China
Duration: 23 Aug 202125 Aug 2021

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th International Joint Conference on Asia-Pacific Web and Web-Age Information Management, APWeb-WAIM 2021

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • CTR prediction
  • Neural network
  • Self-residual embedding


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