On Variational Inference for User Modeling in Attribute-Driven Collaborative Filtering
This work aims to improve recommender systems by more accurately predicting user behavior based on attribute affinities, which is beneficial for e-Commerce platforms.
This paper addresses user modeling in attribute-driven collaborative filtering by using causal inference to learn user-attribute affinities through temporal contexts. The authors formulate this as a probabilistic machine learning problem and apply a variational inference method, demonstrating superior performance on next attribute prediction tasks on two real-world datasets compared to standard baselines.
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.