DFIAM: deep factorization integrated attention mechanism for smart TV recommendation

Springer Science and Business Media LLC
Publication Type:
Journal Article
World Wide Web, 2021, 24, (5), pp. 1465-1481
Issue Date:
Full metadata record
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.
Please use this identifier to cite or link to this item: