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Discovering routines from large-scale human locations using probabilistic topic models
2011
ACM Transactions on Intelligent Systems and Technology
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In this work we discover the daily location-driven routines which are contained in a massive reallife human dataset collected by mobile phones. ...
Topic models are probabilistic generative models for documents that identify the latent structure that underlies a set of words. ...
for real-life routine modeling from large-scale mobile phone data is novel. ...
doi:10.1145/1889681.1889684
fatcat:gllrfrkcpjcdbeuoaqrnplecce
Mining Human Location-Routines Using a Multi-Level Approach to Topic Modeling
2010
2010 IEEE Second International Conference on Social Computing
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In this work we address the problem of modeling varying time duration sequences for large-scale human routine discovery from cellphone sensor data using a multi-level approach to probabilistic topic models ...
We use an unsupervised learning approach that discovers human routines of varying durations ranging from half-hourly to several hours. ...
CONCLUSION In this work we devise a probabilistic multi-level topic model to discover human routines of semantic locations. ...
doi:10.1109/socialcom.2010.71
dblp:conf/socialcom/FarrahiG10
fatcat:qhhuhf7qpfctlkjaod52cnafr4
Probabilistic Mining of Socio-Geographic Routines From Mobile Phone Data
2010
IEEE Journal on Selected Topics in Signal Processing
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We use an unsupervised approach, based on probabilistic topic models, to discover latent human activities in terms of the joint interaction and location behaviors of 97 individuals over the course of approximately ...
We propose a model, called bag of multimodal behavior, that integrates the modeling of variations of location over multiple time-scales, and the modeling of interaction types from proximity. ...
with, our goal is to discover routines from large-scale multimodal phone data. ...
doi:10.1109/jstsp.2010.2049513
fatcat:4wiy5i2y55bjljr74h63emltgi
Extracting Mobile Behavioral Patterns with the Distant N-Gram Topic Model
2012
2012 16th International Symposium on Wearable Computers
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Mining patterns of human behavior from large-scale mobile phone data has potential to understand certain phenomena in society. ...
In this paper, we propose a probabilistic topic model that we call the distant n-gram topic model (DNTM) to address the problem of learning long duration human location sequences. ...
sequence modeling methods. ...
doi:10.1109/iswc.2012.20
dblp:conf/iswc/FarrahiG12
fatcat:wkikka6enzcjrfa5vgytd6gaxu
Discovering human routines from cell phone data with topic models
2008
2008 12th IEEE International Symposium on Wearable Computers
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Using real-life data from the Reality Mining dataset, covering 68 000+ hours of human activities, we can successfully discover location-driven (from cell tower connections) and proximity-driven (from Bluetooth ...
The framework is built from a probabilistic topic model learned on novel bag type representations of activity-related cues (location, proximity and their temporal variations over a day) of peoples' daily ...
Introduction Human activity modeling from large-scale sensor data is an emerging domain in ubiquitous computing towards determining the behaviour and habits of individuals and the structure and dynamics ...
doi:10.1109/iswc.2008.4911580
dblp:conf/iswc/FarrahiG08
fatcat:2mgwnt4rfvds5p5dpfi743fm6u
A probabilistic approach to socio-geographic reality mining
2011
ACM SIGMultimedia Records
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Big thank you to Iacopo and Corinne who helped us in many ways and showed us some special places in Switzerland. ...
We first investigate two types of probabilistic topic models for large-scale location-driven phone data mining. ...
In contrast, our work investigates the human routine discovery task from mobile phone data, on a true large scale, and we further use this data to discover group routines in addition to individual routines ...
doi:10.1145/2069203.2069206
fatcat:b4veqpnsajdrbhm4xvugd3qop4
What did you do today?
2008
Proceeding of the 16th ACM international conference on Multimedia - MM '08
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We present a framework built from two Hierarchical Bayesian topic models to discover human location-driven routines from mobile phones. ...
Using 68 000+ hours of real-life human data from the Reality Mining dataset, we successfully discover various types of routines. ...
Recently, they have been successfully applied to data sources other than text, such as images [6] , video, and genetics, but to our knowledge their use for real-life routine modeling from large-scale ...
doi:10.1145/1459359.1459503
dblp:conf/mm/FarrahiG08
fatcat:fpvbjtk75jemtlmqg3k4glhyye
A probabilistic approach to mining mobile phone data sequences
2013
Personal and Ubiquitous Computing
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The particular problem of interest is the effective mining of long sequences from large-scale location data to be practical for Reality Mining applications, which suffer from large amounts of noise and ...
To address this complex data, we propose an unsupervised probabilistic topic model called the distant n-gram topic model (DNTM). ...
We thank Olivier Bornet (Idiap) for help with location data processing and visualization, Gian Paolo Perrucci (Nokia Research Lausanne) for insights on routine visualization, and Trinh-Minh-Tri Do (Idiap ...
doi:10.1007/s00779-013-0640-8
fatcat:obrxe6rznjbhxk3ui7ewpq6otu
Learning and predicting multimodal daily life patterns from cell phones
2009
Proceedings of the 2009 international conference on Multimodal interfaces - ICMI-MLMI '09
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We present a method that can discover routines from multiple modalities (location and proximity) jointly modeled, and that uses these informative routines to predict unlabeled or missing data. ...
Using a joint representation of location and proximity data over approximately 10 months of 97 individuals' lives, Latent Dirichlet Allocation is applied for the unsupervised learning of topics describing ...
This paper presents an approach for large-scale unsupervised learning and prediction of people routines through the joint modeling of human location and proximity interactions. ...
doi:10.1145/1647314.1647373
dblp:conf/icmi/FarrahiG09
fatcat:k4w3rtcezjhhxn3upie6mxti6u
Friend Recommendation System for Online Social Networks
2016
International Journal of Computer Applications
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Here probability Distribution algorithm is used for extracting lifestyle of users learns from text mining. ...
Admin having all rights of friend Recommendation system, it can add new user, collect data from user name, photo, and lifestyle and also collect data from Google API. ...
Personal Ubiquitous
Computing, 10(4):255-268, To discover daily location
driven routines from large scale location data [16]. ...
doi:10.5120/ijca2016911963
fatcat:4jhztj6gqzgxpdxjl3dsr2qpra
A Semantic based Friends Recommendation System using Socio-Routine
2021
International Journal for Research in Applied Science and Engineering Technology
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Whereas, Recommendation systems for social networks are different from other kinds of system, since the item here are rational human beings rather than goods. ...
In this paper, a social network is formally represented and taking text mining as a perspective, we have proposed a framework that will recommend friend using an efficient Algorithm. ...
Therefore, we adopt the probabilistic topic model to discover the probabilities of hidden "life styles" from the "life documents". ...
doi:10.22214/ijraset.2021.34162
fatcat:awajpelslnfqxb5tqljqlpszgi
Discovering Mobility Patterns on Bicycle-Based Public Transportation System by Using Probabilistic Topic Models
[chapter]
2012
Ambient Intelligence - Software and Applications
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An unsupervised methodology based on probabilistic topic models has been used to achieve these goals. ...
Topic models are probabilistic generative models for documents that identify the latent structure that underlies a set of words. ...
Probabilistic topic models have been used to discover mobility patterns in an unsupervised manner. In particular, Latent Dirichlet Allocation (LDA) has been used. ...
doi:10.1007/978-3-642-28783-1_18
dblp:conf/isami/Montoliu12
fatcat:ld3k57spybgtnjvgzbmlof5ljy
A Better Semantic based Friend Recommendation System for Modern Social Networks
2016
International Journal of Computer Applications
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The user's data is stored in database and lifestyle is extracted using topic model. By constructing friend-matching graph, our system depicts the similarity of lifestyles between two users. ...
People meet new people on social networks from across the world, eventually bringing the world closer. ...
They used an unsupervised approach, based on probabilistic topic models, to discover latent human activities in terms of the joint interaction and location behaviours of 97 individuals over the course ...
doi:10.5120/ijca2016912475
fatcat:c37cg3qesvdc5mhxjxywp3j4xe
Social Network Friend Recommendation System Using Semantic Web
2016
International Journal of Science and Research (IJSR)
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Using text mining, daily activities of users are modelled as a life documents. This life document is used for extracting user's life styles or habits by using Latent Dirichlet Allocation algorithm. ...
This system measures the similarities of life styles, habits, locations or user profiles between users and recommends friends if their life styles , habits, locations or user profiles have higher similarities ...
But this approach is based on two wearable sensor and it is not used with smart phones. To discover lifestyles with the help of smartphones, we used probabilistic topic model. ...
doi:10.21275/v5i1.nov152976
fatcat:fdjpn7tkpvfkjl7ifuadyp57ce
Introduction to the special issue on intelligent systems for activity recognition
2011
ACM Transactions on Intelligent Systems and Technology
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The article "Discovering Routines from Large-Scale Human Locations using Probabilistic Topic Models" presented by Katayoun Farrahi and Daniel Gatica-Perez adopts the "reality mining" data set from MIT ...
media lab for mining daily location-driven routines. ...
doi:10.1145/1889681.1889682
fatcat:y7vxivvkfrhxnf7e4alq4adcsi
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