Hao Qingbo, Zhu Ke, Wang Chundong, Wang Peng, Mo Xiuliang, Liu Zhen
Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China.
School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China.
Soft comput. 2022;26(10):4755-4770. doi: 10.1007/s00500-022-06925-z. Epub 2022 Mar 16.
The rapid development of mobile Internet has spawned various mobile applications (apps). A large number of apps make it difficult for users to choose apps conveniently, causing the app overload problem. As the most effective tool to solve the problem of app overload, app recommendation has attracted extensive attention of researchers. Traditional recommendation methods usually use historical usage data to explore users' preferences and then make recommendations. Although traditional methods have achieved certain success, the performance of app recommendation still needs to be improved due to the following two reasons. On the one hand, it is difficult to construct recommendation models when facing with the sparse user-app interaction data. On the other hand, contextual information has a large impact on users' preferences, which is often overlooked by traditional methods. To overcome the aforementioned problems, we proposed a context-aware feature deep interaction learning (CFDIL) method to explore users' preferences and then perform app recommendation by learning potential user-app relationships in different contexts. The novelty of CFDIL is as follows: (1) CFDIL incorporates contextual features into users' preferences modeling by constructing novel user and app feature portraits. (2) The problem of data sparsity is effectively solved by the use of dense user and app feature portraits, as well as the tensor operations for label sets. (3) CFDIL trains a new deep network structure, which can make accurate app recommendation using the contextual information and attribute information of users and apps. We applied CFDIL on three real datasets and conducted extensive experiments, which shows that CFDIL outperforms the benchmark methods.
移动互联网的快速发展催生了各种移动应用程序（应用）。大量的应用使得用户难以方便地选择应用，从而导致了应用过载问题。作为解决应用过载问题的最有效工具，应用推荐吸引了研究人员的广泛关注。传统的推荐方法通常使用历史使用数据来探索用户的偏好，然后进行推荐。尽管传统方法取得了一定的成功，但由于以下两个原因，应用推荐的性能仍有待提高。一方面，面对稀疏的用户-应用交互数据时，难以构建推荐模型。另一方面，上下文信息对用户的偏好有很大影响，而传统方法往往忽略了这一点。为了克服上述问题，我们提出了一种上下文感知特征深度交互学习（CFDIL）方法，通过学习不同上下文中潜在的用户-应用关系来探索用户偏好，进而进行应用推荐。CFDIL的新颖之处如下：（1）CFDIL通过构建新颖的用户和应用特征画像，将上下文特征纳入用户偏好建模。（2）通过使用密集的用户和应用特征画像以及标签集的张量运算，有效地解决了数据稀疏问题。（3）CFDIL训练了一种新的深度网络结构，该结构可以利用用户和应用的上下文信息和属性信息进行准确的应用推荐。我们将CFDIL应用于三个真实数据集并进行了广泛的实验，结果表明CFDIL优于基准方法。
