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SCUN students achieved good grades in the 2018 TipDM Big Data Mining Race
Home  NEWS
SCUN students achieved good grades in the 2018 TipDM Big Data Mining Race

The result of 2018 TipDM Big Data Mining Race(TipDM Cup) has been revealed recently.The team is composed of 3students, FENG Qi, DENG Mianyu and LI Qiuyu, from School of Mathematics andStatistics, stood out from the 3,016 student teams of more than 300universities nationwide and won the second prize. Meanwhile, SCUN won the bestorganization award.

Feng Qi's team selected B: marketingrecommendation for TV product in this competition. In the age of  information overload, it is a majorchallenge  in data mining to explore therecommendation scheme between information demand person and information producers and establish a recommendation system tobuild the relationship between users and information products . Feng Qi's teamused the collaborative filtering algorithm to create a recommendation scheme,and calculated the recommendation index, recall rate and accuracy rate of eachTV product, and label the user and the product separately for packagerecommendation. This research method is relatively novel and provides new ideasfor TV product marketing.

Figure 2-2: Program-based recommendation systemdiagram

Since the launch of the TipTM Cup inMarch 2018, to help students achieve excellent results, School of Mathematicsand Statistics has vigorously mobilized and organized teachers to providemeticulous guidance to them. In the early stage of the competition, afterofficial release of the topic and some data, the participatingstudents analyzedthe topic and explored the solution under the guidance of the instructor.During the competition, the participating students actively cooperated witheach other, tried hard new schemes, and finally completed the research papersuccessfully and won the judges' affirmation.

TheTipTM Cup was hosted by the CUMUM Organizing Committee andGuangzhou TipTM Intelligent Technology Co., Ltd., which is designed for schoolpostgraduates and undergraduates nationwide. It aims to encourage students tolearn data mining theory knowledge and improve students' comprehensive abilityto solve practical problems by using the data analysis method.

(Editor:admin Source:SCUN)