Generative models for item adoptions using social correlation

Users face many choices on the Web when it comes to choosing which product to buy, which video to watch, etc. In making adoption decisions, users rely not only on their own preferences, but also on friends. We call the latter social correlation which may be caused by the homophily and social influen...

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Main Authors: CHUA, Freddy Chong Tat, LAUW, Hady Wirawan, LIM, Ee Peng
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Language:English
Published: Institutional Knowledge at Singapore Management University 2013
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Online Access:https://ink.library.smu.edu.sg/sis_research/1550
https://ink.library.smu.edu.sg/context/sis_research/article/2549/viewcontent/GenerativeModelsItemAdoptionsSocialCorrelation_2013_afv.pdf
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spelling sg-smu-ink.sis_research-25492018-06-18T04:44:21Z Generative models for item adoptions using social correlation CHUA, Freddy Chong Tat LAUW, Hady Wirawan LIM, Ee Peng Users face many choices on the Web when it comes to choosing which product to buy, which video to watch, etc. In making adoption decisions, users rely not only on their own preferences, but also on friends. We call the latter social correlation which may be caused by the homophily and social influence effects. In this paper, we focus on modeling social correlation on users’ item adoptions. Given a user-user social graph and an item-user adoption graph, our research seeks to answer the following questions: whether the items adopted by a user correlate to items adopted by her friends, and how to model item adoptions using social correlation. We propose a social correlation framework that considers a social correlation matrix representing the degrees of correlation from every user to the user's friends, in addition to a set of latent factors representing topics of interests of individual users. Based on the framework, we develop two generative models, namely sequential and unified, and the corresponding parameter estimation approaches. From each model, we devise the social correlation only and hybrid methods for predicting missing adoption links. Experiments on LiveJournal and Epinions data sets show that our proposed models outperform the approach based on latent factors only (LDA). 2013-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1550 info:doi/10.1109/TKDE.2012.137 https://ink.library.smu.edu.sg/context/sis_research/article/2549/viewcontent/GenerativeModelsItemAdoptionsSocialCorrelation_2013_afv.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Data mining Database applications Database management Information technology and systems Mining methods and algorithms Databases and Information Systems Numerical Analysis and Scientific Computing Social Media
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Data mining
Database applications
Database management
Information technology and systems
Mining methods and algorithms
Databases and Information Systems
Numerical Analysis and Scientific Computing
Social Media
spellingShingle Data mining
Database applications
Database management
Information technology and systems
Mining methods and algorithms
Databases and Information Systems
Numerical Analysis and Scientific Computing
Social Media
CHUA, Freddy Chong Tat
LAUW, Hady Wirawan
LIM, Ee Peng
Generative models for item adoptions using social correlation
description Users face many choices on the Web when it comes to choosing which product to buy, which video to watch, etc. In making adoption decisions, users rely not only on their own preferences, but also on friends. We call the latter social correlation which may be caused by the homophily and social influence effects. In this paper, we focus on modeling social correlation on users’ item adoptions. Given a user-user social graph and an item-user adoption graph, our research seeks to answer the following questions: whether the items adopted by a user correlate to items adopted by her friends, and how to model item adoptions using social correlation. We propose a social correlation framework that considers a social correlation matrix representing the degrees of correlation from every user to the user's friends, in addition to a set of latent factors representing topics of interests of individual users. Based on the framework, we develop two generative models, namely sequential and unified, and the corresponding parameter estimation approaches. From each model, we devise the social correlation only and hybrid methods for predicting missing adoption links. Experiments on LiveJournal and Epinions data sets show that our proposed models outperform the approach based on latent factors only (LDA).
format text
author CHUA, Freddy Chong Tat
LAUW, Hady Wirawan
LIM, Ee Peng
author_facet CHUA, Freddy Chong Tat
LAUW, Hady Wirawan
LIM, Ee Peng
author_sort CHUA, Freddy Chong Tat
title Generative models for item adoptions using social correlation
title_short Generative models for item adoptions using social correlation
title_full Generative models for item adoptions using social correlation
title_fullStr Generative models for item adoptions using social correlation
title_full_unstemmed Generative models for item adoptions using social correlation
title_sort generative models for item adoptions using social correlation
publisher Institutional Knowledge at Singapore Management University
publishDate 2013
url https://ink.library.smu.edu.sg/sis_research/1550
https://ink.library.smu.edu.sg/context/sis_research/article/2549/viewcontent/GenerativeModelsItemAdoptionsSocialCorrelation_2013_afv.pdf
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