Modeling Dynamic User Interests: A Neural Matrix Factorization Approach
This work addresses the challenge of extracting insights from unstructured and dynamic user data for applications like content recommendation, though it appears incremental as it combines existing matrix factorization and neural network techniques.
The authors tackled the problem of modeling dynamic user interests from online content consumption data by proposing a neural matrix factorization model, which achieved superior predictive performance over baseline methods in a case study on Boston Globe readers over five years.
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.