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Displaying 1-17 out of 17 total
Smartphones for Large-Scale Behavior Change Interventions
Found in: IEEE Pervasive Computing
By Neal Lathia,Veljko Pejovic,Kiran K. Rachuri,Cecilia Mascolo,Mirco Musolesi,Peter J. Rentfrow
Issue Date:July 2013
pp. 66-73
Equipped with cutting-edge sensing technology and high-end processors, smartphones can unobtrusively sense human behavior and deliver feedback and behavioral therapy. The authors discuss two applications for behavioral monitoring and change and present UBh...
 
Mining User Mobility Features for Next Place Prediction in Location-Based Services
Found in: 2012 IEEE 12th International Conference on Data Mining (ICDM)
By Anastasios Noulas,Salvatore Scellato,Neal Lathia,Cecilia Mascolo
Issue Date:December 2012
pp. 1038-1043
Mobile location-based services are thriving, providing an unprecedented opportunity to collect fine grained spatio-temporal data about the places users visit. This multi-dimensional source of data offers new possibilities to tackle established research pro...
 
Mining Public Transport Usage for Personalised Intelligent Transport Systems
Found in: Data Mining, IEEE International Conference on
By Neal Lathia, Jon Froehlich, Licia Capra
Issue Date:December 2010
pp. 887-892
Traveller information, route planning, and service updates have become essential components of public transport systems: they help people navigate built environments by providing access to information regarding delays and service disruptions. However, one ...
 
Recommending Social Events from Mobile Phone Location Data
Found in: Data Mining, IEEE International Conference on
By Daniele Quercia, Neal Lathia, Francesco Calabrese, Giusy Di Lorenzo, Jon Crowcroft
Issue Date:December 2010
pp. 971-976
A city offers thousands of social events a day, and it is difficult for dwellers to make choices. The combination of mobile phones and recommender systems can change the way one deals with such abundance. Mobile phones with positioning technology are now w...
 
PURBA 2013: workshop on pervasive urban applications
Found in: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication (UbiComp '13 Adjunct)
By Dominik Dahlem, Francesco Calabrese, Giusy Di Lorenzo, Neal Lathia, Santi Phithakkitnukoon
Issue Date:September 2013
pp. 1183-1188
This is the proposal for the Third Workshop on Pervasive Urban Applications (PURBA 2013). The workshop aims to build on the success of the previous workshops organized in conjunction with the Pervasive 2011 and 2012, to continue to disseminate the results ...
     
Open source smartphone libraries for computational social science
Found in: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication (UbiComp '13 Adjunct)
By Cecilia Mascolo, George Roussos, Kiran Rachuri, Neal Lathia
Issue Date:September 2013
pp. 911-920
The ubiquity of sensor-rich and computationally powerful smartphones makes them an ideal platform for conducting social and behavioural research. However, building sensor data collection tools remains arduous and challenging: it requires an understanding o...
     
Contextual dissonance: design bias in sensor-based experience sampling methods
Found in: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing (UbiComp '13)
By Cecilia Mascolo, Kiran K. Rachuri, Neal Lathia, Peter J. Rentfrow
Issue Date:September 2013
pp. 183-192
The Experience Sampling Method (ESM) has been widely used to collect longitudinal survey data from participants; in this domain, smartphone sensors are now used to augment the context-awareness of sampling strategies. In this paper, we study the effect of ...
     
Personalizing the local mobile experience: workshop at RecSys 2012
Found in: Proceedings of the sixth ACM conference on Recommender systems (RecSys '12)
By Daniele Quercia, Henriette Cramer, Karen Church, Neal Lathia
Issue Date:September 2012
pp. 359-360
Mobile, local recommendations are on the rise. Surprisingly however, research addressing user perceptions of local recommendations and local differences when interacting with such recommendation services is yet scarce. Location-based recommendation service...
     
Using ratings to profile your health
Found in: Proceedings of the sixth ACM conference on Recommender systems (RecSys '12)
By Neal Lathia
Issue Date:September 2012
pp. 303-304
The widespread adoption of mobile technology allows personalised applications to be deployed in an increasing host of contexts; user modelling, profiling, and personalised recommendations are becoming an integral component of mobile information systems. Fu...
     
Using idle moments to record your health via mobile applications
Found in: Proceedings of the 1st ACM workshop on Mobile systems for computational social science (MCSS '12)
By Neal Lathia
Issue Date:June 2012
pp. 22-27
As smart mobile phones permeate society, so too does the opportunity to use these technologies to unobtrusively capture patterns of daily life and interact with people in situ. The ability to record facets of daily life has given rise to the notion of the ...
     
Using control theory for stable and efficient recommender systems
Found in: Proceedings of the 21st international conference on World Wide Web (WWW '12)
By Jun Wang, Neal Lathia, Tamas Jambor
Issue Date:April 2012
pp. 11-20
The aim of a web-based recommender system is to provide highly accurate and up-to-date recommendations to its users; in practice, it will hope to retain its users over time. However, this raises unique challenges. To achieve complex goals such as keeping t...
     
How smart is your smartcard?: measuring travel behaviours, perceptions, and incentives
Found in: Proceedings of the 13th international conference on Ubiquitous computing (UbiComp '11)
By Licia Capra, Neal Lathia
Issue Date:September 2011
pp. 291-300
The widespread adoption of automated fare collection (AFC) systems by public transport authorities around the world means that, increasingly, people carry and use passive sensors (embedded inside of public transit tickets) to record their daily movements. ...
     
Temporal diversity in recommender systems
Found in: Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval (SIGIR '10)
By Licia Capra, Neal Lathia, Stephen Hailes, Xavier Amatriain
Issue Date:July 2010
pp. 210-217
Collaborative Filtering (CF) algorithms, used to build web-based recommender systems, are often evaluated in terms of how accurately they predict user ratings. However, current evaluation techniques disregard the fact that users continue to rate items over...
     
Temporal collaborative filtering with adaptive neighbourhoods
Found in: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (SIGIR '09)
By Licia Capra, Neal Lathia, Stephen Hailes
Issue Date:July 2009
pp. 435-435
Collaborative Filtering aims to predict user tastes, by minimising the mean error produced when predicting hidden user ratings. The aim of a deployed recommender system is to iteratively predict users' preferences over a dynamic, growing dataset, and syste...
     
The wisdom of the few: a collaborative filtering approach based on expert opinions from the web
Found in: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (SIGIR '09)
By Haewoon Kwak, Josep M. Pujol, Neal Lathia, Nuria Oliver, Xavier Amatriain
Issue Date:July 2009
pp. 435-435
Nearest-neighbor collaborative filtering provides a successful means of generating recommendations for web users. However, this approach suffers from several shortcomings, including data sparsity and noise, the cold-start problem, and scalability. In this ...
     
kNN CF: a temporal social network
Found in: Proceedings of the 2008 ACM conference on Recommender systems (RecSys '08)
By Licia Capra, Neal Lathia, Stephen Hailes
Issue Date:October 2008
pp. 1-2
Recommender systems, based on collaborative filtering, draw their strength from techniques that manipulate a set of user-rating profiles in order to compute predicted ratings of unrated items. There are a wide range of techniques that can be applied to thi...
     
The effect of correlation coefficients on communities of recommenders
Found in: Proceedings of the 2008 ACM symposium on Applied computing (SAC '08)
By Licia Capra, Neal Lathia, Stephen Hailes
Issue Date:March 2008
pp. 28-34
Recommendation systems, based on collaborative filtering, offer a means of sifting through the enourmous amounts of content on the web by composing user ratings in order to generate predicted ratings for other users. These kinds of systems can be viewed as...
     
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