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

no code implementations • 10 Jun 2020 • Xianfeng Tang, Yozen Liu, Neil Shah, Xiaolin Shi, Prasenjit Mitra, Suhang Wang

In this paper, we study a novel problem of explainable user engagement prediction for social network Apps.

I Know You'll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application

no code implementations • 29 Sep 2019 • Carl Yang, Xiaolin Shi, Jie Luo, Jiawei Han

Then we design a novel deep learning pipeline based on LSTM and attention to accurately predict user churn with very limited initial behavior data, by leveraging the correlations among users' multi-dimensional activities and the underlying user types.

Clustering

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

1 code implementation • 2 Jun 2019 • Yozen Liu, Xiaolin Shi, Lucas Pierce, Xiang Ren

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.

Time Series Analysis