论文标题
混合深层嵌入具有动态方面级解释的建议
Hybrid Deep Embedding for Recommendations with Dynamic Aspect-Level Explanations
论文作者
论文摘要
由于三个挑战，可解释的建议远非部分解决。首先是偏好学习的个性化，这要求不同的项目/用户对用户偏好或项目质量的学习有不同的贡献。第二个是动态解释，这对于推荐解释的及时性至关重要。最后一个是解释的粒度。实际上，方面级别的解释比项目级或用户级的解释更具说服力。在本文中，为了同时解决这些挑战，我们提出了一个新型模型，称为Hybrid Deep Embedding（HDE），用于基于方面的可解释建议，可以通过动态方面的解释提出建议。 HDE的主要思想是通过融合从评论中提取的动态隐式反馈以及细心的用户 - 项目相互作用来了解生成方面级别解释的动态潜在方面偏好/质量向量的动态嵌入以及动态的潜在方面偏好/质量向量。特别是，由于自动学习用户/项目的方面偏好/质量，因此HDE能够捕获用户或项目评论中未提及的方面的影响。在实际数据集上进行的广泛实验验证了HDE的推荐性能和解释性。我们工作的源代码可在\ url {https://github.com/lola63/hde-python}中获得
Explainable recommendation is far from being well solved partly due to three challenges. The first is the personalization of preference learning, which requires that different items/users have different contributions to the learning of user preference or item quality. The second one is dynamic explanation, which is crucial for the timeliness of recommendation explanations. The last one is the granularity of explanations. In practice, aspect-level explanations are more persuasive than item-level or user-level ones. In this paper, to address these challenges simultaneously, we propose a novel model called Hybrid Deep Embedding (HDE) for aspect-based explainable recommendations, which can make recommendations with dynamic aspect-level explanations. The main idea of HDE is to learn the dynamic embeddings of users and items for rating prediction and the dynamic latent aspect preference/quality vectors for the generation of aspect-level explanations, through fusion of the dynamic implicit feedbacks extracted from reviews and the attentive user-item interactions. Particularly, as the aspect preference/quality of users/items is learned automatically, HDE is able to capture the impact of aspects that are not mentioned in reviews of a user or an item. The extensive experiments conducted on real datasets verify the recommending performance and explainability of HDE. The source code of our work is available at \url{https://github.com/lola63/HDE-Python}
