Explainable Recommender with Geometric Information Bottleneck

Hanqi Yan, Lin Gui, Yulan He

Keywords: Interpretability, Recommender System, Information Extraction

Abstract: Explainable recommender systems have attracted much interest in recent years as they can explain their recommendation decisions, enhancing user trust in the systems. Most explainable recommender systems rely on human-generated rationales or annotated aspect features from user reviews to train models for rational generation or extraction. The rationales produced are often confined to a single review. To avoid the expensive human annotation process and to generate explanations beyond individual reviews, we propose an explainable recommender system trained on user reviews by developing a transferable Geometric Information Bottleneck (GIANT), which leverages the prior knowledge acquired through clustering on a user-item graph built on user-item rating interactions, since graph nodes in the same cluster tend to share common characteristics or preferences. We then feed user reviews and item reviews into a variational network to learn latent topic distributions which are regularised by the distributions of user/item estimated based on their distances to various cluster centroids of the user-item graph. By iteratively refining the instance-level review latent topics with GIANT, our method learns a robust latent space from the text for rating prediction and explanation generation. Experimental results on three e-commerce datasets show that our model significantly improves the interpretability of a variational recommender using a standard Gaussian prior, in terms of coherence, diversity and faithfulness, while achieving performance comparable to existing content-based recommender systems in terms of rating prediction accuracy.

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TL;DR: To consider user-item interactions for an interpretable recommender system, we propose to incorporate the geometric regularisation derived from user-item interaction graphs to learn the latent factors of review text in a variational network.