As a valuable source of user preferences and item properties, reviews have been widely leveraged in many approaches to enhance the performance of recommender systems. Although encouraging success has been obtained, there are two more weaknesses need to be addressed. (1) Most approaches represent users or items merely based on the modeling of review texts, but ignore the potential and latent preferences beyond textual information. (2) Existing methods tend to blindly merge all the previous reviews for user profiling. However, it may be less effective because different interacted items may play distinct roles. Hence, indiscriminately aligning interacted items may limit the model flexibility and performance. In this paper, with the desire to fill these gaps, we design a novel attentive deep review-based recommendation method. In specific, we complement the item representation by an auxiliary vector, based on which a user is then attentively profiled by her posted items to predict the likeness for the target item. Extensive experiments on five real-world datasets demonstrate that our model can not only significantly outperform the state-of-the-art methods, but also provide intuitive explanations to the recommendations.

中文翻译：

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利用评论嵌入和用户关注来推荐商品

作为用户偏好和项目属性的宝贵来源，评论已在许多方法中得到广泛利用，以增强推荐系统的性能。尽管已经获得了令人鼓舞的成功，但还有两个缺点需要解决。（1）大多数方法仅基于评论文本的建模来表示用户或项目，但忽略了文本信息之外的潜在偏好和潜在偏好。（2）现有方法倾向于盲目合并所有先前的评论以进行用户分析。但是，它可能不太有效，因为不同的交互项可能会扮演不同的角色。因此，任意对齐的交互项可能会限制模型的灵活性和性能。在本文中，为了填补这些空白，我们设计了一种新颖的基于深层审查的推荐方法。具体来说，我们通过辅助向量对商品表示法进行补充，然后基于该矢量辅助用户对其发布的商品进行专心分析，以预测目标商品的相似度。在五个真实世界的数据集上进行的广泛实验表明，我们的模型不仅可以大大胜过最新技术，而且可以为建议提供直观的解释。