A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction
A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction
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DOI：
10.24963/ijcai.2018/521
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发表时间：
2018-07
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通讯作者：
Zhiyong Cheng;Ying Ding;Xiangnan He;Lei Zhu;Xuemeng Song;M. Kankanhalli
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作者：
Zhiyong Cheng;Ying Ding;Xiangnan He;Lei Zhu;Xuemeng Song;M. Kankanhalli
当前的推荐系统考虑了项目的各个方面以做出准确的推荐。不同的用户对这些方面的重要性不同，这些方面可以被认为是偏好/注意力权重向量。大多数现有的推荐系统假设，对于一个人，这个向量是相同的所有项目。然而，这种假设通常是无效的，特别是当考虑到用户与不同特征的项目的交互时。为了解决这个问题，在本文中，我们开发了一种新的方面感知推荐模型命名为A$^3$NCF，它可以捕获用户支付给不同项目的不同方面的注意力。具体来说，我们设计了一个新的主题模型，从评论文本中提取用户偏好和项目特征。然后，它们用于1）指导用户和项目的表征学习，以及2）使用注意力网络捕获用户对目标项目的每个方面的特殊注意力。通过在几个大规模数据集上的大量实验，我们证明了我们的模型在评级预测任务中优于最先进的评论感知推荐系统。
Current recommender systems consider the various aspects of items for making accurate recommendations. Different users place different importance to these aspects which can be thought of as a preference/attention weight vector. Most existing recommender systems assume that for an individual, this vector is the same for all items. However, this assumption is often invalid, especially when considering a user's interactions with items of diverse characteristics. To tackle this problem, in this paper, we develop a novel aspect-aware recommender model named A$^3$NCF, which can capture the varying aspect attentions that a user pays to different items. Specifically, we design a new topic model to extract user preferences and item characteristics from review texts. They are then used to 1) guide the representation learning of users and items, and 2) capture a user's special attention on each aspect of the targeted item with an attention network. Through extensive experiments on several large-scale datasets, we demonstrate that our model outperforms the state-of-the-art review-aware recommender systems in the rating prediction task.