  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.
