Causal Embeddings for Recommendation

Abstract

Many current applications use recommendations in order to modify the natural user behavior, such as to increase the number of sales or the time spent on a website. This results in a gap between the final recommendation objective and the classical setup where recommendation candidates are evaluated by their coherence with past user behavior, by predicting either the missing entries in the user-item matrix, or the most likely next event. To bridge this gap, we optimize a recommendation policy for the task of increasing the desired outcome versus the organic user behavior. We show this is equivalent to learning to predict recommendation outcomes under a fully random recommendation policy. To this end, we propose a new domain adaptation algorithm that learns from logged data containing outcomes from a biased recommendation policy and predicts recommendation outcomes according to random exposure. We compare our method against state-of-the-art factorization methods, in addition to new approaches of causal recommendation and show significant improvements.

Accepted as a long paper at the Twelfth ACM Conference on Recommender Systems (RecSys '18), October 2--7, 2018, Vancouver, BC, Canada

References in corpus (1)

• Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation

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• Recommender systems based on graph embedding techniques: A comprehensive review
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• Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations
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• CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation
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• Dynamic Causal Collaborative Filtering
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• Pareto Invariant Representation Learning for Multimedia Recommendation
• Variation Control and Evaluation for Generative SlateRecommendations
• The Importance of Causality in Decision Making: A Perspective on Recommender Systems
• Causality-Inspired Fair Representation Learning for Multimodal Recommendation
• Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendation
• Automatic Feature Fairness in Recommendation via Adversaries
• Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method
• Helen: Optimizing CTR Prediction Models with Frequency-wise Hessian Eigenvalue Regularization