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.
翻译:最近的研究表明,提供个性化解释以及建议可以提高信任度和感知质量。此外,它让用户有机会通过使部分解释具有说服力来改进建议。一方面,目前的推荐者系统将建议、解释和感知目标共同作为模型,但这在他们各自的业绩之间造成了内在的权衡。另一方面,虽然最近的潜伏线性浮标方法建于现有的推荐者系统之上,但是由于每次对话转折的目标最优化,它们却在计算判断方面效率低下。我们用M&MS-VAE来解决这些缺陷,这是一个基于多式联运模型假设的新变异自动编码器,用于建议和解释。我们在一个薄弱的监督系统下,在模拟完全和部分观察到的变量。然后,我们利用经过培训的M&MMS-VAE模型的普及能力将用户偏好和批评分开。我们的工作最重要的创新就是我们以自我监督的方式建立和训练的基模模模模模块,我们以简单的联合排序目标来解决这些缺陷。在四个实体-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币-货币