Context-aware prediction of user's first click

ABSTRACTLocation-based services has attracted attentions from both industry and academia. The development of position tracking technologies and the increasing popularity of smart phones has collected large amounts of contexts. An important issue is how to leverage the rich contexts to predict a user's need accurately. In this paper, we propose a novel approach to predict the product type that a user will first click in an e-commerce application, after they update their location manually. Our proposed approach models the problem as a multi-label classification. We introduce three sets of features including location feature, time feature and behavioral feature. We use the Periodica algorithm [10], which was designed to mine the periodic behaviors of moving objects, to generate a series of periodicity templates. The templates are further exploited as behavioral features. Finally, we design several experiments using a real world dataset collected by an e-commerce application called WuXianGouXiang, which is developed by Nokia Research Center, Beijing and was released in September 2011. We have obtained a dataset from the service logs and the dataset contains over 3000 registered shops and 20000 users. Our experimental results demonstrate that the three sets of features contribute significantly to the classification of different users and the best result is achieved when using all of them.