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The cutting force spectrum of the CNC lathe is the basic data for the reliability design, reliability test and reliability evaluation of the CNC lathe and its components. Due to the complex and changeable turning conditions and different cutting processes, the cutting load presents multi-peak characteristic. In order to compile the CNC lathe cutting force spectrum accurately, a compilation method based on kernel density estimation (KDE) of goodnesssmoothness comprehensive evaluation (G-SCE) is proposed. According to the measured dynamic cutting force, the KDE is used to establish the dynamic cutting force distribution of the CNC lathe. For the rule of thumb method (ROT) based on the mean integrated square error and the least squares cross-validation method (LCV) based on the integrated square error does not take into account the influence of different bandwidths on the goodness estimation and the smoothness of the estimated curve. The estimation accuracy test method based on multiple goodness-of-fit tests, and the smoothness test method based on the envelope curve are proposed. The entropy method is used to comprehensively calculate the estimated goodness index and the smoothness index to determine the optimal bandwidth. The case analysis shows that the method proposed can solve the problem of too large estimation error of parameter distribution for multimodal distribution. At the same time, it can better comprehensively evaluate the KDE under different bandwidths. In short, a new method of optimal bandwidth selection is proposed in the original method.

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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.