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A0206
Title: Two-level monotonic multistage recommender systems Authors:  Ben Dai - The Chinese University of Hong Kong (China) [presenting]
Xiaotong Shen - University of Minnesota (United States)
Wei Pan - University of Minnesota (United States)
Abstract: A recommender system learns to predict the user-specific preference over items, making personalized recommendations based on a relatively small number of observations. One challenging issue is how to leverage three-way interactions, referred to as user-item-stage dependencies on a monotonic chain of events, to enhance the prediction accuracy. A monotonic chain of events occurs, for instance, in an article sharing dataset, where a ``follow'' action implies a ``like'' action, which in turn implies a ``view'' action. We develop a multistage recommender system utilizing a two-level monotonic property for personalized prediction. Particularly, we derive a large-margin classifier based on a nonnegative additive latent factor model, reducing the number of model parameters for personalized prediction while guaranteeing prediction consistency. On this ground, we derive a regularized cost function to learn user-specific behaviors at different stages, linking decision functions to numerical and categorical covariates to model user-item-stage interactions. Computationally, we derive an algorithm based on blockwise coordinate descent. Theoretically, we show that the two-level monotonic property enhances the accuracy of learning as compared to a standard method treating each stage individually and an ordinal method utilizing only one-level monotonicity. Finally, the proposed method compares favorably with existing methods in simulations and an article sharing dataset.