Multi-view factorization machines for mobile app recommendation based on hierarchical attention
Multi-view factorization machines for mobile app recommendation based on hierarchical attention
复制标题
基于分层注意力的移动应用推荐多视图分解机
DOI：
10.1016/j.knosys.2019.06.029
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发表时间：
2020-01-01
影响因子：
8.8
通讯作者：
Wu, Jian
中科院分区：
文献类型：
--
作者：
Liang, Tingting;Zheng, Lei;Wu, Jian
移动的应用推荐已经成为克服移动的应用市场中信息过载的有效解决方案。最近的研究已经证明了神经网络在推荐任务中的强大功能，但很少用于移动的应用程序。作为神经网络的发展之一，基于注意力的推荐模型由于其能够从原始输入中过滤掉无信息特征，在推荐方面取得了很好的效果。本文为了有效预测用户对应用程序的偏好，我们提出了一种用于应用程序推荐的分层神经网络模型，称为MV-AFM，该模型通过注意力机制对来自不同视图的特征的交互（简称视图交互）进行建模。具体来说，MV-AFM的新奇是引入视图分割功能的相互作用和两个层次的注意力网络的建设：特征级的注意力，从每个视图内的特征嵌入开始，打算选择视图的代表性功能，和视图级的注意力，学习任何两个视图之间的相互作用的重要性。在两个真实世界的移动的应用程序数据集上进行的大量实验证明了MV-AFM的有效性。(C)2019由Elsevier B. V.出版
Mobile app recommendation has been an effective solution to overcoming the information overload in mobile app markets. Recent studies have demonstrated the power of neural network in recommendation tasks which is however rarely exploited for mobile apps. As one of the development of neural network, attention-based models have shown promising results for recommendation because of its capability of filtering out uninformative features from raw inputs. In this paper, to effectively predict users' preferences for apps, we propose a hierarchical neural network model called MV-AFM for app recommendation which models the interactions of features from different views (view interactions for short) through the attention mechanism. Specifically, the novelty of MV-AFM is the introduction of view segmentation for feature interactions and the construction of two level attention networks: the feature-level attention, starting from the feature embeddings within each view, which intends to select the representative features for the view, and the view-level attention, which learns the importance of interactions between any two views. Extensive experiments on two real-world mobile app datasets demonstrate the effectiveness of MV-AFM. (C) 2019 Published by Elsevier B.V.