Abstract
Abstract Understanding the online behavior and intent of online visitors is the subject of a long line of research. mechanisms to understand the purchase intent of visitors, to increase the number of visits that end with a purchase. anonymous visitors garner little attention, having no shopping history or known interests. Compared to profiled returning customers whose history is known, anonymous visitors garner less attention. The lack of a known shopping history or interests makes it hard to learn from their behavior, or infer their shopping intent. Here, we suggest the use of products’ popularity trends and visit’s temporal information to infer the purchase intention of anonymous visitors. We model these dynamics and utilize our model to infer purchase intent of visitors of two large real e-commerce retailer sites. Our model identifies online signals for purchase intent that can be used for online purchase prediction.
Published Version
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