location-based topic evolution haiqin yang, shouyuan chen, michael r. lyu, irwin king the chinese...
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Location-Based Topic Evolution
Haiqin Yang, Shouyuan Chen, Michael R. Lyu, Irwin King
The Chinese University of Hong Kong
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Outline
Motivation Location-Based Topic Evolution Model Experiments Conclusion
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Location Information is attainable IPGPS3G, Wi-FiNFC
New Mobile Technologies
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Geo-information
Twitter Typhoon trajectory estimation Earthquake location [Sakaki et
al.,WWW’10] Flickr
Geo-tagged photos [Crandall et al., WWW’09]
Geofolk [Sizov, WSDM’10]
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New Applications-Timeliness
Identify users’ interests in a region
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New Applications-Commercial Value
Determine appropriate marketing strategy
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Solution-Topics Learning
Topics: Distributions over words Location-associated documents
Geo-informaiton with message, posts, tags
Help to learn the topics more accurately
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Current Problems
Do not consider appearance and disappearance of topics
Do not model topic evolution Have to determine the number of
topics Location-aware Topic Model [Wang et al.
GIR’07] Geofolk [Sizov, WSDM’10] Geographical topic discovery [Yin et al.
WWW’11]8
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Our Contributions
Propose a location-based topic evolution (LBTE) model Model topic changes of users’ interests
in a region Allow for appearance and disappearance
of topics Automatically determine topic numbers
Efficient inference
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Problem Setup
Vocabulary: Data:
Objective: modeling the topics of data with an unknown number of topics and parameters.
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Assumptions
Documents from unknown topics Topic from hidden
functions, determined by the function value
Functions from a probability measure
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Evolution with Regions
Domains of functions include regions Values of functions represent topics
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Evolution with Regions and Time
The beginning (end) of function domain correspond to appearance (disappearance) of a topic
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Generative Process
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Inference-Gibbs Sampler
1. Sample auxiliary variables: To determine whether the domain of the function contain the region (Bernoulli)
2. Sample assignment: Calculate the probability of assigning to existing function and that of assigning to a new function
3. Draw topics parameters15
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Experiments
Datasets Synthetic data Flickr data
Comparison methods Dirichlet Process Mixture (DPM) Location-Based Topic Evolution (LBTE)
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Synthetic Data
Topics Generation Topics Initialization-Two topics
Center: Parameter:
Topics Evolution Die off rate 40% New topic follows Poisson distribution with parameter
0.8. Location-associated Documents Generation
10 documents for each topic Location of each documents follows the uniform
distribution at the center of the topic with radius, 5 Values of topics follow
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Results of Synthetic Data
LBTE outperforms the DPM at all the time stamps
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LBTE recovers true topics and achieves zero variation of information
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Flickr Data
Geo-tagged photos crawled from 2009/01/01 to 2010/01/01
Only in USA territory.
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An example{ "date": "2009-07-07 19:34:04", "lat": "36.058961", "lon": "-112.083442", "id": "5919764020", "tags": [ "grandcanyon", "nationalpark", "sunset", "limestone", "scenic"] }
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Results of National Park
Topics learned from DPM are scattered
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Results of National Park LBTE utilizes location information and
discovers topics based on the regions
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Yellow Stone
Grand Canyon
Big Bend
Joshua Tree
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Results of National Park
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Conclusion
Advantages of Location-based Topic Evolution Model Automatically modeling the number of
total topics Automatically modeling topics’
appearance and disappearance Succinct sampling-Gibbs sampling
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Thank you !
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Sample Auxiliary Variables
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Sample Assignment