Deadline: 2019-08-15 Award: $25,000 Open to: Everyone*
Though business operations become more and more complex, what we always want to answer is an essential question: how to seize the shopping attitudes and behavior characteristics of consumers in a dynamic interactive environment. We thus can maximize different goals based on the view of consumers through the studies of their shopping chain paths and characteristics. Most E-commerce and retail companies achieve these targets by leveraging the power of data and boosting sales by implementing recommender systems on their websites. In short, these systems aim to predict users’ interests and recommend items that quite likely are interesting for them.
However, user behaviors often vary with both various observed and unobserved factors, such as branding, promotions, individual moods, offline interactions, etc. Therefore, prediction of user behavior diversities in a dynamic interactive environment has been a long existing challenge for any IT or big data company. On the other hand, unprecedented rich data, user interaction and feedbacks of Taobao, China’s largest E-Commerce platform, may provide a chance to tackle this challenge. If contestants in this competition can discover reasonable approaches to predict user behavior diversities in such a complex online environment, all IT and big data companies might be able to adopt such approaches to boost prosperity of the online ecologies.