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http://dx.doi.org/10.5626/KTCP.2015.21.2.154

Smartphone-User Interactive based Self Developing Place-Time-Activity Coupled Prediction Method for Daily Routine Planning System  

Lee, Beom-Jin (Seoul National Univ.)
Kim, Jiseob (Seoul National Univ.)
Ryu, Je-Hwan (Seoul National Univ.)
Heo, Min-Oh (Seoul National Univ.)
Kim, Joo-Seuk (MSC)
Zhang, Byoung-Tak (Seoul National Univ.)
Publication Information
KIISE Transactions on Computing Practices / v.21, no.2, 2015 , pp. 154-159 More about this Journal
Abstract
Over the past few years, user needs in the smartphone application market have been shifted from diversity toward intelligence. Here, we propose a novel cognitive agent that plans the daily routines of users using the lifelog data collected by the smart phones of individuals. The proposed method first employs DPGMM (Dirichlet Process Gaussian Mixture Model) to automatically extract the users' POI (Point of Interest) from the lifelog data. After extraction, the POI and other meaningful features such as GPS, the user's activity label extracted from the log data is then used to learn the patterns of the user's daily routine by POMDP (Partially Observable Markov Decision Process). To determine the significant patterns within the user's time dependent patterns, collaboration was made with the SNS application Foursquare to record the locations visited by the user and the activities that the user had performed. The method was evaluated by predicting the daily routine of seven users with 3300 feedback data. Experimental results showed that daily routine scheduling can be established after seven days of lifelogged data and feedback data have been collected, demonstrating the potential of the new method of place-time-activity coupled daily routine planning systems in the intelligence application market.
Keywords
machine learning; reinforcement learning; location-based service; intelligent App;
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Times Cited By KSCI : 1  (Citation Analysis)
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