GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors
Interacting with Computers
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
How much can behavioral targeting help online advertising?
Proceedings of the 18th international conference on World wide web
Java-ML: A Machine Learning Library
The Journal of Machine Learning Research
A Markov chain model for integrating behavioral targeting into contextual advertising
Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising
A unified approach to building hybrid recommender systems
Proceedings of the third ACM conference on Recommender systems
Collaborative filtering with temporal dynamics
Communications of the ACM
Temporal recommendation on graphs via long- and short-term preference fusion
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining periodic behaviors for moving objects
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
An effective approach for mining mobile user habits
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Action prediction and identification from mining temporal user behaviors
Proceedings of the fourth ACM international conference on Web search and data mining
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Location-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.