Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Proceedings of the 1st ACM international workshop on Wireless mobile applications and services on WLAN hotspots
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Sensing and Modeling Human Networks using the Sociometer
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Extracting places from traces of locations
ACM SIGMOBILE Mobile Computing and Communications Review
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Adaptive Data Fusion for Energy Efficient Routing in Wireless Sensor Networks
IEEE Transactions on Computers
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
SmokeScreen: flexible privacy controls for presence-sharing
Proceedings of the 5th international conference on Mobile systems, applications and services
Discovering personally meaningful places: An interactive clustering approach
ACM Transactions on Information Systems (TOIS)
CenceMe: injecting sensing presence into social networking applications
EuroSSC'07 Proceedings of the 2nd European conference on Smart sensing and context
Collaborative machine learning
From Integrated Publication and Information Systems to Virtual Information and Knowledge Environments
Learning and recognizing the places we go
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Redpin - adaptive, zero-configuration indoor localization through user collaboration
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
Proceedings of the 6th ACM conference on Embedded network sensor systems
Transforming the social networking experience with sensing presence from mobile phones
Proceedings of the 6th ACM conference on Embedded network sensor systems
Challenges in ubiquitous context recognition with personal mobile devices
Proceedings of the 4th ACM International Workshop on Context-Awareness for Self-Managing Systems
PlaceComm: A framework for context-aware applications in place-based virtual communities
Journal of Ambient Intelligence and Smart Environments
Distributed velocity-dependent protocol for multihop cellular sensor networks
EURASIP Journal on Wireless Communications and Networking
Proceedings of the 13th international conference on Ubiquitous computing
Balancing energy, latency and accuracy for mobile sensor data classification
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
CoMon: cooperative ambience monitoring platform with continuity and benefit awareness
Proceedings of the 10th international conference on Mobile systems, applications, and services
Automatically characterizing places with opportunistic crowdsensing using smartphones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
On heterogeneity in mobile sensing applications aiming at representative data collection
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Personal and Ubiquitous Computing
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People-centric sensor-based applications targeting mobile device users offer enormous potential. However, learning inference models in this setting is hampered by the lack of labeled training data and appropriate feature inputs. Data features that lead to better classification models are not available at all devices due to device heterogeneity. Even for devices that provide superior data features, models require sufficient training data, perhaps manually labeled by users, before they work well. We propose opportunistic feature vector merging, and the social-network-driven sharing of training data and models between users. Model and training data sharing within social circles combine to reduce the user effort and time involved in collecting training data to attain the maximum classification accuracy possible for a given model, while feature vector merging can enable a higher maximum classification accuracy by enabling better performing models even for more resource-constrained devices. We evaluate our proposed techniques with a significant places classifier that infers and tags locations of importance to a user based on data gathered from cell phones.