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abstract

Predicting Users' Gender and Age based on Mobile Tasks

Published: 15 February 2022 Publication History

Abstract

Demographic attributes are a key factor in marketing products and services, which enable a business owner to find the ideal customer. Users' app usage behaviors could reveal rich clues regarding their personal attributes since they always determine what apps to use depending on their personal needs and interests. Prior studies [1, 2] have tried to predict users' gender and age through their app usage behavior. However, most of the existing methods for users' demographic prediction are straightforward, simply using popular used apps or app usage frequency as features, without considering the internal semantic relationship of apps usage.
Recently, mobile tasks [3] have been identified from mobile app usage logs, representing a more accurate unit for capturing users' goals and behavioral insights, where a "mobile task" can be thought of as a group of related used apps to accomplish a single discrete task. For example, to plan dinner with friends, multiple apps (e.g., WhatsApp, Yelp, Uber and Google Maps) might be accessed for completing the task. In this talk, I will introduce how we leverage the fine-grained task units for generating user representation aims at predicting users' gender and age. We analyzed the effectiveness of using tasks to infer users' demographics, especially when compared to only treating apps independently. We explored different approaches for constructing users' representation and models with both mobile apps and tasks. Finally, we validated that the two-level hierarchical structure of "apps to tasks" and "tasks to users" is an important factor that should be taken into consideration for improving mobile user modelling. This work shed light on whether and how the extracted mobile tasks could be effectively applied. We believe that the task-based representations could be further explored for improving many other applications.

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MP4 File (WSDM22-smart01.mp4)
App usage behaviours always reveal personal needs and interests, which could be leveraged for inferring users? demographics. However, most of the existing methods for predicting users' gender and age are straightforward, such as simply using app lists or app usage frequency as features, without considering the internal semantic relationship of apps usage. In this video, we introduce how to leverage the fine-grained task units for generating user representation aims at users? demographic prediction. By experimenting on a real-world large-scale dataset, we validate that additional signals brought by tasks and the hierarchical structure between apps and tasks can effectively improve the performance of age and gender.

References

[1]
Malmi, Eric, and Ingmar Weber. "You are what apps you use: Demographic prediction based on user's apps." Proceedings of the International AAAI Conference on Web and Social Media. Vol. 10. No. 1. 2016.
[2]
Seneviratne, Suranga, et al. "Your installed apps reveal your gender and more!." ACM SIGMOBILE Mobile Computing and Communications Review 18.3 (2015): 55--61.
[3]
Tian, Yuan, et al. "Identifying tasks from Mobile App usage patterns." Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020.

Cited By

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  • (2024)Analysis of User Experience in Applications Madrasah Digital Report Website at MTSs Assa'adah Cicurug2024 International Conference on Information Management and Technology (ICIMTech)10.1109/ICIMTech63123.2024.10780917(1-6)Online publication date: 28-Aug-2024

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cover image ACM Conferences
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
February 2022
1690 pages
ISBN:9781450391320
DOI:10.1145/3488560
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Publication History

Published: 15 February 2022

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Author Tags

  1. apps
  2. demographics
  3. mobile users
  4. tasks
  5. user modelling

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  • (2024)Analysis of User Experience in Applications Madrasah Digital Report Website at MTSs Assa'adah Cicurug2024 International Conference on Information Management and Technology (ICIMTech)10.1109/ICIMTech63123.2024.10780917(1-6)Online publication date: 28-Aug-2024

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