A Latent Feelings-aware RNN Model for User Churn Prediction with Behavioral Data

by   Meng Xi, et al.

Predicting user churn and taking personalized measures to retain users is a set of common and effective practices for online game operators. However, different from the traditional user churn relevant researches that can involve demographic, economic, and behavioral data, most online games can only obtain logs of user behavior and have no access to users' latent feelings. There are mainly two challenges in this work: 1. The latent feelings, which cannot be directly observed in this work, need to be estimated and verified; 2. User churn needs to be predicted with only behavioral data. In this work, a Recurrent Neural Network(RNN) called LaFee (Latent Feeling) is proposed, which can get the users' latent feelings while predicting user churn. Besides, we proposed a method named BMM-UCP (Behavior-based Modeling Method for User Churn Prediction) to help models predict user churn with only behavioral data. The latent feelings are names as satisfaction and aspiration in this work. We designed experiments on a real dataset and the results show that our methods outperform baselines and are more suitable for long-term sequential learning. The latent feelings learned are fully discussed and proven meaningful.


page 7

page 8


Non-parametric clustering over user features and latent behavioral functions with dual-view mixture models

We present a dual-view mixture model to cluster users based on their fea...

A relevance-scalability-interpretability tradeoff with temporally evolving user personas

The current work characterizes the users of a VoD streaming space throug...

Midwifery Learning and Forecasting: Predicting Content Demand with User-Generated Logs

Every day, 800 women and 6,700 newborns die from complications related t...

My Mouse, My Rules: Privacy Issues of Behavioral User Profiling via Mouse Tracking

This paper aims to stir debate about a disconcerting privacy issue on we...

Your Favorite Gameplay Speaks Volumes about You: Predicting User Behavior and Hexad Type

In recent years, the gamification research community has widely and freq...

Assessing Anonymized System Logs Usefulness for Behavioral Analysis in RNN Models

System logs are a common source of monitoring data for analyzing computi...

Composite Behavioral Modeling for Identity Theft Detection in Online Social Networks

In this work, we aim at building a bridge from poor behavioral data to a...

Please sign up or login with your details

Forgot password? Click here to reset