Predicting multiple demographic attributes with task specific embedding transformation and attention network

Raehyun Kim, Hyunjae Kim, Janghyuk Lee, Jaewoo Kang

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

Abstract

Most companies utilize demographic information to develop their strategy in a market. However, such information is not available to most retail companies. Several studies have been conducted to predict the demographic attributes of users from their transaction histories, but they have some limitations. First, they focused on parameter sharing to predict all attributes but capturing task-specific features is also important in multi-task learning. Second, they assumed that all transactions are equally important in predicting demographic attributes. However, some transactions are more useful than others for predicting a certain attribute. Furthermore, decision making process of models cannot be interpreted as they work in a black-box manner. To address the limitations, we propose an Embedding Transformation Network with Attention (ETNA) model which shares representations at the bottom of the model structure and transforms them to task-specific representations using a simple linear transformation method. In addition, we can obtain more informative transactions for predicting certain attributes using the attention mechanism. The experimental results show that our model outperforms the previous models on all tasks. In our qualitative analysis, we show the visualization of attention weights, which provides business managers with some useful insights.

Original languageEnglish
Title of host publicationSIAM International Conference on Data Mining, SDM 2019
PublisherSociety for Industrial and Applied Mathematics Publications
Pages765-773
Number of pages9
ISBN (Electronic)9781611975673
Publication statusPublished - 2019 Jan 1
Event19th SIAM International Conference on Data Mining, SDM 2019 - Calgary, Canada
Duration: 2019 May 22019 May 4

Publication series

NameSIAM International Conference on Data Mining, SDM 2019

Conference

Conference19th SIAM International Conference on Data Mining, SDM 2019
CountryCanada
CityCalgary
Period19/5/219/5/4

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ASJC Scopus subject areas

  • Software

Cite this

Kim, R., Kim, H., Lee, J., & Kang, J. (2019). Predicting multiple demographic attributes with task specific embedding transformation and attention network. In SIAM International Conference on Data Mining, SDM 2019 (pp. 765-773). (SIAM International Conference on Data Mining, SDM 2019). Society for Industrial and Applied Mathematics Publications.