Harnessing Dynamic Interests of Crowd in Chinese Online Shopping Festivals

Online shopping is popular in promotion festivals like Singles' Day in China. With the presence of a flash crowd of customers during the limited time in E-commerce websites, the traditional system provides multi-dimensional product ranking lists including popularity based bought lists and viewed lists due to the customers' conformity to the popular products. However, the overloaded information may cause a long path to purchase for customers, who can not efficiently buy satisfied products within the time-limited festivals. To shorten customers' purchasing path, this paper designs an interests-based ranking method to automatically and efficiently aggregate an unified ranking list, by analyzing thousands of customers' shopping behavior to harness the dynamic and wide interests of crowd. Initially, we transform the local interaction behavior of flash crowd into the interests of crowd, which then is further personalized to an unified ranking for each customer. We combine user's temporal interests and current crowd wisdom to generate a new personalized unified ranking. The combined ranking is continuously adjusted with the recent fixed past time window data iteratively according to the evolution growth of crowd. An implementation test is carried out on a real world trace in large-scale interaction logs, which is from Alibaba Tmall with 12 million users and 29 thousand brands. It shows that combining the user's temporal interests and the aggregated crowd wisdom ranking improve the quality of item recommendations distinctly.