The development of the digital economy and internet finance greatly impacts the technological innovation of Chinese commercial banks. Therefore, most of the Chinese commercial banks have invested a lot of money in the new information technology applications to adapt to the impacts of internet finance. Consequently, whether these investments have achieved the expected effect and efficiency has been concerned by these banks. For these concerns, we have used relative data from 49 Chinese commercial banks from 2010 to 2020 period and used the DEA-BCC model and DEA-Malmquist index to analyze their innovation efficiency from both static and dynamic perspectives. Our study found that from the static perspective, the innovation efficiency greatly varies among the banks in the study, and most banks have room for improvement in pure technical efficiency and scale efficiency. From the dynamic perspective, the innovation efficiency of the banks in the study is generally on the rise. Changes in technical efficiency play a major role in improving their overall efficiency, and technological progress is the second driving force. Therefore, technological investment should be increased, and technological levels should be improved to improve innovation efficiency. Our study also shows that, according to the ownership types, different groups of banks have differently shown their innovation efficiencies. Joint-stock commercial banks have the highest innovation efficiency, while rural commercial banks have the lowest innovation efficiency. Our study suggests that all Chinese commercial banks should overcome their own shortages in information technological innovation capability to elevate their innovation efficiency, under the background of internet finance.
The collaborative filtering recommendation system has been widely used in E-commerce as a relatively successful recommendation system. At present, the focus of collaborative filtering recommendation research is mainly on how to improve the accuracy of recommendation by improving the recommendation algorithm. However, in real world, the user’s rating behaviour is not perfectly rational. It is no odd that there is deviation of rating to any a given item for a user in real evaluation. In this case, what it means for the improvement of collaborative filtering recommendation methods, and how they performed when we use the commonly used quality metrics to evaluate the collaborative filtering recommendation methods? In view of these problems, this paper presupposes that the user’s rating behaviour is a bounded rational behaviour, and based on the normality hypothesis of rating error, introduces a simulated rating experiment innovatively to analyse the effect that rating prediction can achieve in sense of the commonly used quality metrics. This study is of significance for the research and application of collaborative filtering recommendation technology.
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