In the last decade, the digital age has sharply redefined the way we study human behavior. With the advancement of data storage and sensing technologies, electronic records now encompass a diverse spectrum of human activity, ranging from location data [1,2], phone [3,4], and email communication [5] to Twitter activity [6] and opensource contributions on Wikipedia and OpenStreetMap [7,8]. In particular, the study of the shopping and mobility patterns of individual consumers has the potential to give deeper insight into the lifestyles and infrastructure of the region. Credit card records (CCRs) provide detailed insight into purchase behavior and have been found to have inherent regularity in consumer shopping patterns [9]; call detail records (CDRs) present new opportunities to understand human mobility [10], analyze wealth [11], and model social network dynamics [12].

Chapter Contents:

• 5.1 Mining shopping and mobility patterns
• 5.1.1 Prediction of shopping behavior with data sparsity
• 5.1.2 Adding contextual information to location data
• 5.1.3 Multi-perspective lifestyles
• 5.2 Data
• 5.3 Discovering shopping patterns
• 5.4 Mobility pattern extraction
• 5.4.1 Extracting cellular tower location types
• 5.4.2 Baseline methods
• 5.4.2.1 Regression on average amount spent
• 5.4.2.2 Classification of primary shopping behavior
• 5.4.3 Characterizing mobility patterns
• 5.5 Predicting shopping behavior
• 5.5.1 Collective matrix factorization
• 5.6 Results
• 5.6.1 Prediction
• 5.6.2 Dual lifestyles
• 5.7 Discussion
• Acknowledgments
• References

Inspec keywords: human factors; recommender systems; consumer behaviour; ubiquitous computing; Web sites

Other keywords: human behavior; model social network dynamics; email communication; urban lifestyles; recommender systems; data storage; electronic records; OpenStreetMap; sensing technologies; Wikipedia; urban computing

Subjects: Information networks; Search engines; Marketing computing