Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples
This work addresses interpretability and effectiveness issues in recommender systems for users and developers, but it appears incremental as it builds on existing contrastive learning and counterfactual methods.
The authors tackled the problem of modeling monotonicity between neural network outputs and numerical features in recommender systems to improve interpretability and effectiveness, proposing a model-agnostic contrastive learning framework with counterfactual sample synthesizing (CCSS) that was tested on public and industrial datasets and deployed in a large-scale system serving over hundreds of millions of users.
We propose a general model-agnostic Contrastive learning framework with Counterfactual Samples Synthesizing (CCSS) for modeling the monotonicity between the neural network output and numerical features which is critical for interpretability and effectiveness of recommender systems. CCSS models the monotonicity via a two-stage process: synthesizing counterfactual samples and contrasting the counterfactual samples. The two techniques are naturally integrated into a model-agnostic framework, forming an end-to-end training process. Abundant empirical tests are conducted on a publicly available dataset and a real industrial dataset, and the results well demonstrate the effectiveness of our proposed CCSS. Besides, CCSS has been deployed in our real large-scale industrial recommender, successfully serving over hundreds of millions users.