Communications of the ACM
Using Sequential and Non-Sequential Patterns in Predictive Web Usage Mining Tasks
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Cross-Domain Mediation in Collaborative Filtering
UM '07 Proceedings of the 11th international conference on User Modeling
A comparison of Bayesian estimators for unsupervised Hidden Markov Model POS taggers
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Factorizing personalized Markov chains for next-basket recommendation
Proceedings of the 19th international conference on World wide web
Persuasive recommendation: serial position effects in knowledge-based recommender systems
PERSUASIVE'07 Proceedings of the 2nd international conference on Persuasive technology
IEEE Transactions on Knowledge and Data Engineering
An MDP-based recommender system
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Using temporal data for making recommendations
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Compression of individual sequences via variable-rate coding
IEEE Transactions on Information Theory
Predicting purchase behaviors from social media
Proceedings of the 22nd international conference on World Wide Web
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An incisive understanding of personal psychological traits is not only essential to many scientific disciplines, but also has a profound business impact on online recommendation. Recent studies in psychology suggest that novelty-seeking trait is highly related to consumer behavior. In this paper, we focus on understanding individual novelty-seeking trait embodied at different levels and across heterogeneous domains. Unlike the questionnaire-based methods widely adopted in the past, we first present a computational framework, Novel Seeking Model (NSM), for exploring the novelty-seeking trait implied by observable activities. Then, we explore the novelty-seeking trait in two heterogeneous domains: check-in behavior in location based social networks, which reflects mobility patterns in the physical world, and online shopping behavior on e-commerce sites, which reflects consumption concepts in economic activities. To demonstrate the effectiveness of NSM, we conducted extensive experiments, with a large dataset covering the two-domain activities for hundreds of thousands of individuals. Our results suggest that NSM offers a powerful paradigm for 1) presenting an effective measurement of a personality trait that can explicitly explain the deviation of individuals from the habits of individuals and crowds; 2) uncovering the correlation of novelty-seeking trait at different levels and across heterogeneous domains. The proposed method provides emerging implications for personalized cross-domain recommendation and targeted advertising.