# Positive and Negative Critiquing for VAE-based Recommenders

arXiv: https://arxiv.org/abs/2204.02162 · HTML: https://mlsanity.com/paper/2204.02162/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 5 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset](https://mlsanity.com/paper/2002.06854/) (2020)
- [Interacting with Explanations through Critiquing](https://mlsanity.com/paper/2005.11067/) (2020)
- [Fast Multi-Step Critiquing for VAE-based Recommender Systems](https://mlsanity.com/paper/2105.00774/) (2021)
- [Multi-Step Critiquing User Interface for Recommender Systems](https://mlsanity.com/paper/2107.06416/) (2021)

## Abstract (arXiv)

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.