Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of Snapchat

06/02/2019
by   Yozen Liu, et al.
0

While mobile social apps have become increasingly important in people's daily life, we have limited understanding on what motivates users to engage with these apps. In this paper, we answer the question whether users' in-app activity patterns help inform their future app engagement (e.g., active days in a future time window)? Previous studies on predicting user app engagement mainly focus on various macroscopic features (e.g., time-series of activity frequency), while ignoring fine-grained inter-dependencies between different in-app actions at the microscopic level. Here we propose to formalize individual user's in-app action transition patterns as a temporally evolving action graph, and analyze its characteristics in terms of informing future user engagement. Our analysis suggested that action graphs are able to characterize user behavior patterns and inform future engagement. We derive a number of high-order graph features to capture in-app usage patterns and construct interpretable models for predicting trends of engagement changes and active rates. To further enhance predictive power, we design an end-to-end, multi-channel neural model to encode temporal action graphs, activity sequences, and other macroscopic features. Experiments on predicting user engagement for 150k Snapchat new users over a 28-day period demonstrate the effectiveness of the proposed models. The prediction framework is deployed at Snapchat to deliver real world business insights. Our proposed framework is also general and can be applied to other social app platforms.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 2

06/02/2021

What and How long: Prediction of Mobile App Engagement

User engagement is crucial to the long-term success of a mobile app. Sev...
06/10/2020

Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps

With the rapid growth and prevalence of social network applications (App...
04/21/2020

SedVis: Supporting Sedentary Behavior Change by Visualizing Personal Mobility Patterns and Action Planning on Smartphone

Prolonged sedentary behavior is related to a number of risk factors for ...
01/25/2021

Predicting Exercise Adherence and Physical Activity in Older Adults Based on Tablet Engagement: A Post-hoc Study

Sufficient physical activity can prolong the ability of older adults to ...
02/25/2018

I'll Be Back: On the Multiple Lives of Users of a Mobile Activity Tracking Application

Mobile health applications that track activities, such as exercise, slee...
01/14/2019

Characterizing and Predicting Email Deferral Behavior

Email triage involves going through unhandled emails and deciding what t...
10/23/2021

Towards User Engagement Dynamics in Social Networks

The engagement of each user in a social network is an essential indicato...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.