Positive and Negative Critiquing for VAE-based Recommenders

• URL: http://arxiv.org/abs/2204.02162v1
• Date: Tue, 5 Apr 2022 12:40:53 GMT
• Title: Positive and Negative Critiquing for VAE-based Recommenders
• Authors: Diego Antognini and Boi Faltings
• Abstract summary: We propose M&Ms-VAE, which achieves state-of-the-art performance in terms of recommendation, explanation, and critiquing. M&Ms-VAE and similar models allow users to negatively critique (i.e., explicitly disagree) We address this deficiency with M&Ms-VAE+, an extension of M&Ms-VAE that enables positive and negative critiquing.
• Score: 39.38032088973816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Providing explanations for recommended items allows users to refine the recommendations by critiquing parts of the explanations. As a result of revisiting critiquing from the perspective of multimodal generative models, recent work has proposed M&Ms-VAE, which achieves state-of-the-art performance in terms of recommendation, explanation, and critiquing. M&Ms-VAE and similar models allow users to negatively critique (i.e., explicitly disagree). However, they share a significant drawback: users cannot positively critique (i.e., highlight a desired feature). We address this deficiency with M&Ms-VAE+, an extension of M&Ms-VAE that enables positive and negative critiquing. In addition to modeling users' interactions and keyphrase-usage preferences, we model their keyphrase-usage dislikes. Moreover, we design a novel critiquing module that is trained in a self-supervised fashion. Our experiments on two datasets show that M&Ms-VAE+ matches or exceeds M&Ms-VAE in recommendation and explanation performance. Furthermore, our results demonstrate that representing positive and negative critiques differently enables M&Ms-VAE+ to significantly outperform M&Ms-VAE and other models in positive and negative multi-step critiquing.

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