Title: Large-scale constrained joint modeling with applications to freemium mobile games
Authors: Gourab Mukherjee - University of Southern California (United States) [presenting]
Abstract: A Constrained Extremely Zero Inflated Joint (CEZIJ) modeling framework is developed for simultaneously analyzing player activity, engagement and drop-outs in app-based mobile freemium games. The proposed framework addresses the complex interdependencies between a player's decision to use a freemium product, the extent of her direct and indirect engagement with the product, and her decision to permanently drop its usage. CEZIJ extends the existing class of joint models for longitudinal and survival data in several ways. It not only accommodates extremely zero-inflated responses in a joint model setting, but also incorporates domain-specific, convex structural constraints on the model parameters. Longitudinal data from app-based mobile games usually exhibit a large set of potential predictors and choosing the relevant set of predictors is highly desirable for various purposes, including improved predictability. To achieve this goal, CEZIJ conducts simultaneous, coordinated selection of fixed and random effects in high-dimensional penalized generalized linear mixed models. For analyzing such large-scale datasets, variable selection and estimation is conducted via a distributed computing based split-and-conquer approach that massively increases scalability and provides better predictive performance over competing predictive methods.