Bayesian Critiquing with Keyphrase Activation Vectors for VAE-based Recommender Systems

Critiquing is a method for conversational recommendation that incrementally adapts recommendations in response to user preference feedback. Recent advances in critiquing have leveraged the power of VAE-CF recommendation in a critiquable-explainable (CE-VAE) framework that updates latent user preference embeddings based on their critiques of keyphrase-based explanations. However, the CE-VAE has two key drawbacks: (i) it uses a second VAE head to facilitate explanations and critiquing, which can sacrifice recommendation performance of the first VAE head due to multiobjective training, and (ii) it requires iterating an inverse decoding-encoding loop for multi-step critiquing that yields poor performance. To address these deficiencies, we propose a novel Bayesian Keyphrase critiquing VAE (BK-VAE) framework that builds on the strengths of VAE-CF, but avoids the problematic second head of CE-VAE. Instead, the BK-VAE uses a Concept Activation Vector (CAV) inspired approach to determine the alignment of item keyphrase properties with latent user preferences in VAE-CF. BK-VAE leverages this alignment in a Bayesian framework to model uncertainty in a user's latent preferences and to perform posterior updates to these preference beliefs after each critique --- essentially achieving CE-VAE's explanation and critique inversion through a simple application of Bayes rule. Our empirical evaluation on two datasets demonstrates that BK-VAE matches or dominates CE-VAE in both recommendation and multi-step critiquing performance.