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

    3 Citations (Scopus)

    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
    DOIs
    Publication statusPublished - 2019
    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
    Country/TerritoryCanada
    CityCalgary
    Period19/5/219/5/4

    ASJC Scopus subject areas

    • Software

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