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Recommender Systems have become an integral part of online e-Commerceplatforms, driving customer engagement and revenue. Most popular recommendersystems attempt to learn from users' past engagement data to understandbehavioral traits of users and use that to predict future behavior. In thiswork, we present an approach to use causal inference to learn user-attributeaffinities through temporal contexts. We formulate this objective as aProbabilistic Machine Learning problem and apply a variational inference basedmethod to estimate the model parameters. We demonstrate the performance of theproposed method on the next attribute prediction task on two real worlddatasets and show that it outperforms standard baseline methods.

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