Multi Task Learning: A Survey and Future Directions

Taeho Lee, Junhee Seok

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Multi-task learning (MTL) is a problem that must be applied in modern recommendation systems and is just as difficult. In the recent e-commerce advertising market, it is necessary to be able to predict not only the probability of users clicking, but also the probability of conversion and purchase. By predicting multi-task, it is possible to increase the accuracy of each task and optimize advertisements for various goals of advertisers. Traditional conversion rate (CVR) prediction models have difficulty learning because the number of conversions is too small compared to the total number of impressions. This problem is called a data sparsity (DS) problem. Another problem is that CVR models trained with samples of clicked impressions infer on samples of all impressions. This problem is called a sample selection bias (SSB) problem. This paper is a summary of the various solutions and current limitations and further directions about solving sample selection bias problem and data sparsity problem.

Original languageEnglish
Title of host publication5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages232-235
Number of pages4
ISBN (Electronic)9781665456456
DOIs
Publication statusPublished - 2023
Event5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023 - Virtual, Online, Indonesia
Duration: 2023 Feb 202023 Feb 23

Publication series

Name5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023

Conference

Conference5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023
Country/TerritoryIndonesia
CityVirtual, Online
Period23/2/2023/2/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • conversion rate prediction
  • data sparsity
  • multi-task learning
  • recommendation systems
  • sample selection bias

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing
  • Decision Sciences (miscellaneous)
  • Information Systems and Management
  • Artificial Intelligence

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