Explainable recommendation, which provides explanations about why an item is recommended, has attracted growing attention in both research and industry communities. However, most existing explainable recommendation methods cannot provide multi-model explanations consisting of both textual and visual modalities or adaptive explanations tailored for the user’s dynamic preference, potentially leading to the degradation of customers’ satisfaction, confidence and trust for the recommender system. On the technical side, Recurrent Neural Network (RNN) has become the most prevalent technique to model dynamic user preferences. Benefit from the natural characteristics of RNN, the hidden state is a combination of long-term dependency and short-term interest to some degrees. But it works like a black-box and the monotonic temporal dependency of RNN is not sufficient to capture the user’s short-term interest.

In this paper, to deal with the above issues, we propose a novel Attentive Recurrent Neural Network (Ante-RNN) with textual and visual fusion for the dynamic explainable recommendation. Specifically, our model jointly learns image representations with textual alignment and text representations with topical attention mechanism in a parallel way. Then a novel dynamic contextual attention mechanism is incorporated into Ante-RNN for modelling the complicated correlations among recent items and strengthening the user’s short-term interests. By combining the full latent visual-semantic alignments and a hybrid attention mechanism including topical and contextual attentions, Ante-RNN makes the recommendation process more transparent and explainable. Extensive experimental results on two real world datasets demonstrate the superior performance and explainability of our model.

可解释的推荐提供了为什么推荐某个条目的解释，在研究界和行业界都引起了越来越多的关注。但是，大多数现有的可解释的推荐方法不能提供由文本和视觉形式组成的多模型解释，也不能提供针对用户的动态偏好量身定制的自适应解释，从而可能导致顾客对推荐系统的满意度，信心和信任度下降。在技​​术方面，递归神经网络（RNN）已成为对动态用户偏好进行建模的最流行技术。受益于RNN的自然特性，隐藏状态在某种程度上是长期依赖和短期利益的组合。

在本文中，为解决上述问题，我们提出了一种新颖的Attentive Recurrent Neural Network（Ante-RNN），将文本和视觉融合为动态可解释的推荐。具体而言，我们的模型以并行方式共同学习具有文本对齐方式的图像表示和具有主题注意机制的文本表示。然后，将一种新颖的动态上下文关注机制整合到Ante-RNN中，以对最近项目之间的复杂关联进行建模并增强用户的短期兴趣。通过将完整的潜在视觉语义对齐方式与包括主题和上下文注意事项的混合注意机制相结合，Ante-RNN使推荐过程更加透明和可解释。