PortraitAI: A Deep Learning-Based Approach for Generating User Portrait for Online Dating Website

DOI

10.2991/assehr.k.200401.058How to use a DOI?

Keywords

deep neural network, user portrait, multi-task learning

Abstract

User portrait is an important technique which could be widely adopted in many real-world applications. It refers to the task of estimating user’s attributes containing their potential preferences and thus could be used to make better recommendations. Online dating Website is one of the most popular web applications which usually has more than 100 million users. Users of such Website are usually required to write down a brief self-introduction as well as fill in important attributes which are then used to make the friend recommendation. However, such data generally contains a good number of missing data or fake information. To cope with this challenge, this paper proposes a deep learning-based approach to generate user portrait merely from user’s self-introduction. Then, a multi-task deep neural network is adopted to simultaneously estimate five kinds of attributes, e.g., age, gender, education, salary and characteristics, from the input textual data. The experimental dataset is collected from one famous dating Website in China. Both the proposed approach as well as three benchmark approaches are implemented and the results are reported. The performance of the proposed approach is superior to the compared method with respect to accuracy.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ziao Wang
AU  - Xiaofeng Zhang
AU  - Xiaofei Yang
AU  - Shaokai Wang
AU  - Wu Xia
PY  - 2020
DA  - 2020/04/06
TI  - PortraitAI: A Deep Learning-Based Approach for Generating User Portrait for Online Dating Website
BT  - Proceedings of the International Conference on Education, Economics and Information Management (ICEEIM 2019)
PB  - Atlantis Press
SP  - 270
EP  - 274
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.200401.058
DO  - 10.2991/assehr.k.200401.058
ID  - Wang2020
ER  -