research-article

Know your personalization: learning topic level personalization in online services

Authors:

Anirban Majumder,

Nisheeth ShrivastavaAuthors Info & Claims

WWW '13: Proceedings of the 22nd international conference on World Wide Web

Pages 873 - 884

https://doi.org/10.1145/2488388.2488464

Published: 13 May 2013 Publication History

Abstract

Online service platforms (OSPs), such as search engines, news-websites, ad-providers, etc., serve highly personalized content to the user, based on the profile extracted from her history with the OSP. In this paper, we capture OSP's personalization for an user in a new data structure called the personalization vector (?), which is a weighted vector over a set of topics, and present efficient algorithms to learn it.

Our approach treats OSPs as black-boxes, and extracts η by mining only their output, specifically, the personalized (for an user) and vanilla (without any user information) contents served, and the differences in these content. We believe that such treatment of OSPs is a unique aspect of our work, not just enabling access to (so far hidden) profiles in OSPs, but also providing a novel and practical approach for retrieving information from OSPs by mining differences in their outputs.

We formulate a new model called Latent Topic Personalization (LTP) that captures the personalization vector in a learning framework and present efficient inference algorithms for determining it. We perform extensive experiments targeting search engine personalization, using data from both real Google users and synthetic setup. Our results indicate that LTP achieves high accuracy (R-pre = 84%) in discovering personalized topics.For Google data, our qualitative results demonstrate that the topics determined by LTP for a user correspond well to his ad-categories determined by Google.

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Huu PTangworakitthaworn PGilbert L(2021)Towards Self-Regulated Individual Learning Path Generation Using Outcome Taxonomies and Constructive Alignment2021 IEEE International Conference on Engineering, Technology & Education (TALE)10.1109/TALE52509.2021.9678777(465-472)Online publication date: 5-Dec-2021
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Nguyen PTangworakitthaworn PGilbert L(2020)Individual Learning Effectiveness Based on Cognitive Taxonomies and Constructive Alignment2020 IEEE REGION 10 CONFERENCE (TENCON)10.1109/TENCON50793.2020.9293733(1002-1006)Online publication date: 16-Nov-2020
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Index Terms

Know your personalization: learning topic level personalization in online services

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cover image ACM Other conferences

WWW '13: Proceedings of the 22nd international conference on World Wide Web

May 2013

1628 pages

ISBN:9781450320351

DOI:10.1145/2488388

General Chairs:
Daniel Schwabe
PUC-Rio - Brazil
,
Virgílio Almeida
UFMG - Brazil
,
Hartmut Glaser
CGI.br - Brazil
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Labs - Spain & Chile
,
Sue Moon
KAIST - South Korea

Copyright © 2013 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
CGIBR: Comite Gestor da Internet no Brazil

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2013

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WWW '13: 22nd International World Wide Web Conference

May 13 - 17, 2013

Rio de Janeiro, Brazil

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WWW '13 Paper Acceptance Rate 125 of 831 submissions, 15%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Lv YDong XJin BZhuo W(2022)Modeling User Profiles Through Multiple Types of User Interaction BehaviorsWeb Information Systems Engineering – WISE 202110.1007/978-3-030-90888-1_42(557-564)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-90888-1_42
Huu PTangworakitthaworn PGilbert L(2021)Towards Self-Regulated Individual Learning Path Generation Using Outcome Taxonomies and Constructive Alignment2021 IEEE International Conference on Engineering, Technology & Education (TALE)10.1109/TALE52509.2021.9678777(465-472)Online publication date: 5-Dec-2021
https://doi.org/10.1109/TALE52509.2021.9678777
Nguyen PTangworakitthaworn PGilbert L(2020)Individual Learning Effectiveness Based on Cognitive Taxonomies and Constructive Alignment2020 IEEE REGION 10 CONFERENCE (TENCON)10.1109/TENCON50793.2020.9293733(1002-1006)Online publication date: 16-Nov-2020
https://doi.org/10.1109/TENCON50793.2020.9293733
Liu JLiu CBelkin N(2020)Personalization in text information retrievalJournal of the Association for Information Science and Technology10.1002/asi.2423471:3(349-369)Online publication date: 28-Jan-2020
https://dl.acm.org/doi/10.1002/asi.24234
Zhang YHu CHu YKasturi TRamasamy SGillingham MYamamoto K(2019)Large-Scale Gender/Age Prediction of Tumblr Users2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)10.1109/ICMLA.2019.00128(712-717)Online publication date: Dec-2019
https://doi.org/10.1109/ICMLA.2019.00128
Knipping-Sorokin RStumpf T(2018)Radikal Online - Das Internet und die Radikalisierung von Jugendlichenkommunikation@gesellschaft10.15460/kommges.2018.19.3.60619:3Online publication date: 1-Nov-2018
https://doi.org/10.15460/kommges.2018.19.3.606
Christakopoulou KBeutel ALi RJain SChi EGuo YFarooq F(2018)Q&RProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3219819.3219894(139-148)Online publication date: 19-Jul-2018
https://dl.acm.org/doi/10.1145/3219819.3219894
Lu HNiu WCaverlee JCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)Learning Geo-Social User Topical Profiles with Bayesian Hierarchical User FactorizationThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210044(205-214)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210044
Cao CGe HLu HHu XCaverlee JKando NSakai TJoho HLi Hde Vries AWhite R(2017)What Are You Known For?Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3080820(743-752)Online publication date: 7-Aug-2017
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Akbari MChua Tde Rijke MShokouhi MTomkins AZhang M(2017)Leveraging Behavioral Factorization and Prior Knowledge for Community Discovery and ProfilingProceedings of the Tenth ACM International Conference on Web Search and Data Mining10.1145/3018661.3018693(71-79)Online publication date: 2-Feb-2017
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