Although Chinese economy has made rapid progress since the reform and opening-up, the universality of contemporary finance has only spread from the emergence of the digital economy. Before the emergence of digital economy, Chinese residents without credit cards would not be allowed to obtain credit records through credit card consumption, nor would they be able to obtain funds from the traditional financial market, which greatly restricted the development of the national economy. However, after the rise of the digital economy, relevant institutions can construct credit information based on the payment software and online behavior records. This is the first time I felt the help of the digital economy for those who were originally excluded from traditional economic behaviors. Besides, I also realized that the era of digital economy brought about by big data and the Internet revolution has arrived. Considering the practical help of the digital economy and the inevitable trend of future development, I decide to apply for the master's program in Digital Economy of [University Name].

During my internship in [University Name]go Interactive Entertainment Technology Co., Ltd, since the launch of “Xiao[University Name]g” App, the newly developed e-commerce platform of the company, the sparse user behavior data led to low extraction accuracy and low accuracy of the recommendation system, which resulted in a low conversion rate for users to repurchase recommended goods after purchasing goods. With insufficient data and low extraction accuracy, I used the influence set of the current object to increase the rating density of the item and the cloud model comparing the similarity of user preferences at the knowledge level to solve the problem. In addition, considering the shortcomings of traditional association rule technology, when analyzing user purchasing preferences, I adopted two-way association rules to mine consumption preferences. I used SQL to extract the user data for a month and cleaned the data to delete the log records unrelated to the commodities and delete the commodities that the users last visited. At the same time, in order to exclude the occasional browsing behavior of individual users, I only kept the log records of the number of products accessed by users during the session. The data is divided into training set and test set, and the variable X is introduced to represent the proportion. Different from the traditional association rules, this time we used two indicators, precision and coverage, to measure. The product weight is set as the function of the [University Name]go users' normalized browsing time and single path browsing frequency of the product, and the weighted Aprior mining algorithm is used to mine the frequent item set with Python's Pandas and Numpy libraries. The minimum confidence threshold is set as 0.75, and the minimum support threshold is set as 0.20, thus generating two-way association rules. Two weeks after the new association rules were put into operation, the conversion rate for recommended bit items increased by 10%. During this internship, I realized the user value in the era of digital economy, and the study of user behavior and consumption decision is closely related to the development of digital products, which also made me clear about my career planning.

I plan to go to the Internet company to work in a data operation post upon graduation and apply the knowledge of digital analysis and data [University Name]agement to the actual product operation of enterprises. Three years later, I will form a set of my own user behavior analysis system in this position, bringing the actual user growth and platform expansion to the company in the project. After five years, I will shift to the direction of data-oriented [University Name]agement. I will lead the team to use data tools and methods to [University Name]age products, develop new products, and provide business guidance and strategic planning [University Name]agement for the team. The Digital Economy MSc at [University Name] attracts me because of its flexible curriculum setting. It only sets up three required courses and the rest minor courses are available for students to choose freely; it allows me to tailor my learning according to my own needs. Compared with other schools that focus on digital services and intellectual property in the field of media, this program focuses on big data and economy in the digital economy. Specifically, I aspire to study Micro Perspectives on the Digital Economy and Macro Perspectives on the Digital Economy modules, which will equip me with macroscopic and microscopic analysis methods and theories in the field of Digital Economy and in Crowds and Clouds - Digital Ecosystems. In the Social Media, Marketing and Internet Culture module, I aspire to learn the theory of consumer behavior and the methodology and dimension of Social Media analysis in Social Media. All of these modules will ready me for my career plan.

I am looking forward to studying in the [University Name].