A Context-Aware User-Item Representation Learning for Item Recommendation
A Context-Aware User-Item Representation Learning for Item Recommendation
复制标题
用于项目推荐的上下文感知用户项目表示学习
DOI：
10.1145/3298988
复制
发表时间：
2019-03-01
影响因子：
5.6
通讯作者：
Luo, Xiangyang
中科院分区：
文献类型：
--
作者：
Wu, Libing;Quan, Cong;Luo, Xiangyang
评论和用户-项目交互（即，评级分数）已经被广泛地用于用户评级预测。然而，这些现有的技术主要提取的潜在表示的用户和项目在一个独立的和静态的方式。也就是说，一个单一的静态特征向量被导出来编码用户偏好，而不考虑每个候选项的特定特性。我们认为，这种静态编码方案是无法完全捕捉用户的喜好，因为用户通常表现出不同的偏好时，与不同的项目进行交互。在这篇文章中，我们提出了一种新的上下文感知的用户项目表示学习模型的评级预测，命名为CARL。CARL基于用户-项目对的各个潜在特征和潜在特征交互，为给定的用户-项目对导出联合表示。然后，CARL采用因式分解机进一步建模的基础上的用户项目对评分预测的高阶特征的相互作用。具体而言，两个独立的学习组件设计在CARL利用审查数据和交互数据，分别：基于审查的特征学习和基于交互的特征学习。在基于评论的学习组件中，通过卷积运算和注意力机制，通过联合考虑它们对应的评论来提取给定用户-项目对的基于对的相关特征。然而，这些功能只是审查驱动的，可能并不全面。因此，基于交互的学习组件还基于用户-项目对从单独的交互数据中提取补充特征。最后的评级分数，然后得出一个动态的线性融合机制。在7个真实世界数据集上的实验表明，CARL比现有的最先进的替代品实现了更好的评级预测准确性。此外，与注意机制，我们表明，基于对的相关信息（即，上下文感知信息）可以被突出显示以解释不同用户-项目对的评级预测。
Both reviews and user-item interactions (i.e., rating scores) have been widely adopted for user rating prediction. However, these existing techniques mainly extract the latent representations for users and items in an independent and static manner. That is, a single static feature vector is derived to encode user preference without considering the particular characteristics of each candidate item. We argue that this static encoding scheme is incapable of fully capturing users’ preferences, because users usually exhibit different preferences when interacting with different items. In this article, we propose a novel context-aware user-item representation learning model for rating prediction, named CARL. CARL derives a joint representation for a given user-item pair based on their individual latent features and latent feature interactions. Then, CARL adopts Factorization Machines to further model higher order feature interactions on the basis of the user-item pair for rating prediction. Specifically, two separate learning components are devised in CARL to exploit review data and interaction data, respectively: review-based feature learning and interaction-based feature learning. In the review-based learning component, with convolution operations and attention mechanism, the pair-based relevant features for the given user-item pair are extracted by jointly considering their corresponding reviews. However, these features are only reivew-driven and may not be comprehensive. Hence, an interaction-based learning component further extracts complementary features from interaction data alone, also on the basis of user-item pairs. The final rating score is then derived with a dynamic linear fusion mechanism. Experiments on seven real-world datasets show that CARL achieves significantly better rating prediction accuracy than existing state-of-the-art alternatives. Also, with the attention mechanism, we show that the pair-based relevant information (i.e., context-aware information) in reviews can be highlighted to interpret the rating prediction for different user-item pairs.