Modeling Dynamic User Interests: A Neural Matrix Factorization Approach

TL;DR

This paper introduces a neural matrix factorization model that captures the nonlinear, dynamic nature of user interests in online content consumption, providing interpretable insights and superior predictive accuracy.

Contribution

It combines matrix factorization with neural networks to model nonlinear, dynamic user interests, enabling efficient, interpretable analysis of large-scale text consumption data.

Findings

Model effectively captures dynamic user interests.

Outperforms baseline methods in predictive accuracy.

Provides coherent, nuanced consumption pattern insights.

Abstract

In recent years, there has been significant interest in understanding users' online content consumption patterns. But, the unstructured, high-dimensional, and dynamic nature of such data makes extracting valuable insights challenging. Here we propose a model that combines the simplicity of matrix factorization with the flexibility of neural networks to efficiently extract nonlinear patterns from massive text data collections relevant to consumers' online consumption patterns. Our model decomposes a user's content consumption journey into nonlinear user and content factors that are used to model their dynamic interests. This natural decomposition allows us to summarize each user's content consumption journey with a dynamic probabilistic weighting over a set of underlying content attributes. The model is fast to estimate, easy to interpret and can harness external data sources as an empirical prior. These advantages make our method well suited to the challenges posed by modern datasets. We use our model to understand the dynamic news consumption interests of Boston Globe readers over five years. Thorough qualitative studies, including a crowdsourced evaluation, highlight our model's ability to accurately identify nuanced and coherent consumption patterns. These results are supported by our model's superior and robust predictive performance over several competitive baseline methods.