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DFIAM: deep factorization integrated attention mechanism for smart TV recommendation
World Wide Web ( IF 3.7 ) Pub Date : 2021-07-21 , DOI: 10.1007/s11280-021-00924-0
Yijie Zhou 1, 2 , Dingguo Yu 1, 2, 3 , Xuewen Shen 3 , Suiyu Zhang 3 , Guandong Xu 4
Affiliation  

Users are frequently overwhelmed by their uninterested programs due to the development of smart TV and the excessive number of programs. For addressing this issue, various recommendation methods have been introduced to TV fields. In TV content recommendation, auxiliary information, such as users’ personality traits and program features, greatly influences their program preferences. However, existing methods always fail to take auxiliary information into account. In this paper, aiming at personality program recommendation on smart TV platforms, we propose a novel Deep Factorization Integrated Attention Mechanism (DFIAM) model, which fully takes advantage of users’ personality traits, program and interaction features to construct users’ preference representations. DFIAM consists of two components, FNN component and DMF component. By suitably exploiting auxiliary information, FNN component devises a feature-interaction layer to capture the low- and higher-order feature interactions, while DMF component has a field-interaction layer to acquire higher-order field interactions. The embedding layer is divided into two layers , including feature embedding layer and field embedding layer. The two components share the feature embedding layer to profile latent representations of user and program features to reduce learning parameters and computational complexity. And the field embedding layer calculated by feature embedding layer is the input of DMF component. Besides, hierarchical attention networks are applied to self-adapt the influence of each feature and effectively capture more important feature interactions. To evaluate the performance of the DFIAM model, extensive experiments are conducted on two real-world datasets from different scenarios. The results of our proposed model have outperformed the mainstream neural network-based recommendation models in terms of RMSE, MAE and R-square.



中文翻译:

DFIAM:用于智能电视推荐的深度分解综合注意力机制

由于智能电视的发展和节目的过多,用户经常被他们不感兴趣的节目所淹没。为了解决这个问题,各种推荐方法已经被引入到电视领域。在电视内容推荐中,用户的性格特征、节目特点等辅助信息对他们的节目偏好影响很大。然而,现有的方法总是没有考虑到辅助信息。在本文中,针对智能电视平台上的个性节目推荐,我们提出了一种新颖的深度分解综合注意机制(DFIAM)模型,该模型充分利用用户的个性特征、节目和交互特征来构建用户的偏好表示。DFIAM 由两个组件组成,FNN 组件和 DMF 组件。通过适当地利用辅助信息,FNN 组件设计了一个特征交互层来捕获低阶和高阶特征交互,而 DMF 组件具有一个场交互层来获取高阶场交互。嵌入层分为两层,包括特征嵌入层和场嵌入层。这两个组件共享特征嵌入层以分析用户和程序特征的潜在表示,以减少学习参数和计算复杂性。而特征嵌入层计算的场嵌入层是DMF组件的输入。此外,分层注意力网络用于自适应每个特征的影响并有效捕获更重要的特征交互。为了评估 DFIAM 模型的性能,对来自不同场景的两个真实世界数据集进行了广泛的实验。我们提出的模型的结果在 RMSE、MAE 和 R-square 方面优于主流的基于神经网络的推荐模型。

更新日期:2021-07-22
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