Abstract
The purpose of this paper aims to proposal a new idea to know the customers with using Chinese input method on smartphone better based on the big data analysis. Nowadays, China has the largest group of smartphone users. Everyday hundreds of millions of people get used to using their smartphones shopping, texting and searching for information and so on. A more efficient Chinese input method will definitely enhance their user experience and speed up the transactions. Furthermore, based on the massive user’s behavior data analysis through Baidu Index, it can be identified the target-users attribute for the Chinese input method, and advance a more user-friendly Chinese input method of smartphones considering the social-cultural factors affecting users. So, it’s meaningful to cluster the users and specify the user-based design features. Firstly, the most popular Chinese input method tools are introduced. Secondly, the research on the Chinese input method is analyzed to find the current research limitation. Thirdly, considering the limitation of the research on the Chinese input method, a big data analysis platform-Baidu Index is selected in order to research on the people’s attributes and geographical distribution of using smartphone’s Chinese input method. The conclusion and discussion are at the end of the paper.
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Acknowledgments
This work is partially funded by the National Natural Science Foundation of China (71301061;71503103); Natural Science Foundation of Jiangsu Province (BK20150157); Social Science Foundation of Jiangsu Province (14GLC008); The research base of Chinese IOT development strategy (133930), The Fundamental Research Funds for the Central Universities (JUSRP11583; 2015JDZD004); Funding of Jiangsu Innovation Program for Graduate Education(KYZZ16_0305).
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Zuo, W., Wang, Y., Li, Y. (2019). How to Get to Know Your Customers Better? A Case Analysis of Smartphone Users with Chinese Input Method Based on Baidu Index. In: Ahram, T., Falcão, C. (eds) Advances in Usability, User Experience and Assistive Technology. AHFE 2018. Advances in Intelligent Systems and Computing, vol 794. Springer, Cham. https://doi.org/10.1007/978-3-319-94947-5_13
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