Recent studies have shown that providing personalized explanations alongside recommendations increases trust and perceived quality. Furthermore, it gives users an opportunity to refine the recommendations by critiquing parts of the explanations. On one hand, current recommender systems model the recommendation, explanation, and critiquing objectives jointly, but this creates an inherent trade-off between their respective performance. On the other hand, although recent latent linear critiquing approaches are built upon an existing recommender system, they suffer from computational inefficiency at inference due to the objective optimized at each conversation's turn. We address these deficiencies with M&Ms-VAE, a novel variational autoencoder for recommendation and explanation that is based on multimodal modeling assumptions. We train the model under a weak supervision scheme to simulate both fully and partially observed variables. Then, we leverage the generalization ability of a trained M&Ms-VAE model to embed the user preference and the critique separately. Our work's most important innovation is our critiquing module, which is built upon and trained in a self-supervised manner with a simple ranking objective. Experiments on four real-world datasets demonstrate that among state-of-the-art models, our system is the first to dominate or match the performance in terms of recommendation, explanation, and multi-step critiquing. Moreover, M&Ms-VAE processes the critiques up to 25.6x faster than the best baselines. Finally, we show that our model infers coherent joint and cross generation, even under weak supervision, thanks to our multimodal-based modeling and training scheme.

中文翻译：

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基于VAE的推荐系统的快速多步批注

最近的研究表明，在提供建议的同时提供个性化的说明可以提高信任度和感知质量。此外，它为用户提供了通过对部分解释进行批判来完善建议的机会。一方面，当前的推荐器系统共同对推荐，说明和批评目标进行建模，但这在它们各自的性能之间产生了固有的权衡。另一方面，尽管最近的潜在线性批判方法是建立在现有推荐系统上的，但由于在每次对话时都要进行优化的目标，因此它们在推理上会遇到计算效率低下的问题。我们使用M＆Ms-VAE来解决这些缺陷，M＆Ms-VAE是一种新颖的变体自动编码器，用于基于多模态建模假设的建议和解释。我们在弱监督方案下训练模型，以模拟全部和部分观测到的变量。然后，我们利用受过训练的M＆Ms-VAE模型的泛化能力分别嵌入用户偏好和评论。我们工作最重要的创新是我们的评分模块，该模块以自我监督的方式构建和培训，并具有简单的排名目标。在四个真实数据集上进行的实验表明，在最先进的模型中，我们的系统是第一个在推荐，说明和多步骤批注方面支配或匹配性能的系统。此外，M＆Ms-VAE处理批注的速度比最佳基准快25.6倍。最后，我们证明了即使在弱监督下，我们的模型也可以推断出连贯的联合和交叉生成，