Two-level monotonic multistage recommender systems

TL;DR

This paper introduces a two-level monotonic multistage recommender system that leverages hierarchical event dependencies to improve personalized prediction accuracy, especially with high missing data, through a novel large-margin classifier and efficient algorithm.

Contribution

It develops a new multistage recommender model based on a two-level monotonic property, incorporating a large-margin classifier and a blockwise coordinate descent algorithm, with theoretical and empirical validation.

Findings

Enhances prediction accuracy over standard methods.

Effectively handles high missing data between stages.

Outperforms existing methods in simulations and real datasets.

Abstract

A recommender system learns to predict the user-specific preference or intention over many items simultaneously for all users, making personalized recommendations based on a relatively small number of observations. One central 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. In this article, we develop a multistage recommender system utilizing a two-level monotonic property characterizing a monotonic chain of events for personalized prediction. Particularly, we derive a large-margin classifier based on a nonnegative additive latent factor model in the presence of a high percentage of missing observations, particularly between stages, 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.