Modeling Dynamic User Interests: A Neural Matrix Factorization Approach

Paramveer S. Dhillon () and Sinan Aral ()
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Paramveer S. Dhillon: School of Information, University of Michigan, Ann Arbor, Michigan 48109
Sinan Aral: MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142

Marketing Science, 2021, vol. 40, issue 6, 1059-1080

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 data sets used by digital marketers. 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.

Keywords: machine learning; deep learning; natural language processing; digital marketing; user profiling (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:40:y:2021:i:6:p:1059-1080

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