On Variational Inference for User Modeling in Attribute-Driven Collaborative Filtering

2020-12-02 · Venugopal Mani, Ramasubramanian Balasubramanian, Sushant Kumar, Abhinav Mathur, Kannan Achan

Recommender Systems have become an integral part of online e-Commerce platforms, driving customer engagement and revenue. Most popular recommender systems attempt to learn from users' past engagement data to understand behavioral traits of users and use that to predict future behavior. In this work, we present an approach to use causal inference to learn user-attribute affinities through temporal contexts. We formulate this objective as a Probabilistic Machine Learning problem and apply a variational inference based method to estimate the model parameters. We demonstrate the performance of the proposed method on the next attribute prediction task on two real world datasets and show that it outperforms standard baseline methods.

📄 PDF Abstract BibTeX arXiv:2012.01577

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AttributeBIG-bench Machine Learning Causal Inference Collaborative Filtering Recommendation SystemsVariational Inference

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