Abstract
Despite widespread adoption, recommender systems remain mostly black boxes. Recently, providing explanations about why items are recommended has attracted increasing attention due to
its capability to enhance user trust and satisfaction. In this paper, we propose a co-attentive multitask learning model for explainable recommendation. Our model improves both prediction accuracy and explainability of recommendation by fully
exploiting the correlations between the recommendation task and the explanation task. In particular,
we design an encoder-selector-decoder architecture
inspired by human’s information-processing model
in cognitive psychology. We also propose a hierarchical co-attentive selector to effectively model
the cross knowledge transferred for both tasks. Our
model not only enhances prediction accuracy of the
recommendation task, but also generates linguistic explanations that are fluent, useful, and highly
personalized. Experiments on three public datasets
demonstrate the effectiveness of our model