research-article

Mining novelty-seeking trait across heterogeneous domains

Authors:

Nicholas Jing Yuan,

Xing XieAuthors Info & Claims

WWW '14: Proceedings of the 23rd international conference on World wide web

Pages 373 - 384

https://doi.org/10.1145/2566486.2567976

Published: 07 April 2014 Publication History

Abstract

An incisive understanding of personal psychological traits is not only essential to many scientific disciplines, but also has a profound business impact on online recommendation. Recent studies in psychology suggest that novelty-seeking trait is highly related to consumer behavior. In this paper, we focus on understanding individual novelty-seeking trait embodied at different levels and across heterogeneous domains. Unlike the questionnaire-based methods widely adopted in the past, we first present a computational framework, Novel Seeking Model (NSM), for exploring the novelty-seeking trait implied by observable activities. Then, we explore the novelty-seeking trait in two heterogeneous domains: check-in behavior in location based social networks, which reflects mobility patterns in the physical world, and online shopping behavior on e-commerce sites, which reflects consumption concepts in economic activities. To demonstrate the effectiveness of NSM, we conducted extensive experiments, with a large dataset covering the two-domain activities for hundreds of thousands of individuals. Our results suggest that NSM offers a powerful paradigm for 1) presenting an effective measurement of a personality trait that can explicitly explain the deviation of individuals from the habits of individuals and crowds; 2) uncovering the correlation of novelty-seeking trait at different levels and across heterogeneous domains. The proposed method provides emerging implications for personalized cross-domain recommendation and targeted advertising.

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Hazrati NRicci F(2023)Choice models and recommender systems effects on users’ choicesUser Modeling and User-Adapted Interaction10.1007/s11257-023-09366-x34:1(109-145)Online publication date: 18-May-2023
https://doi.org/10.1007/s11257-023-09366-x
Doan TSahebi S(2020)TransCrossCF: Transition-based Cross-Domain Collaborative Filtering2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA51294.2020.00059(320-327)Online publication date: Dec-2020
https://doi.org/10.1109/ICMLA51294.2020.00059
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Index Terms

Mining novelty-seeking trait across heterogeneous domains
1. Applied computing
  1. Law, social and behavioral sciences
    1. Psychology
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Other conferences

WWW '14: Proceedings of the 23rd international conference on World wide web

April 2014

926 pages

ISBN:9781450327442

DOI:10.1145/2566486

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

Copyright © 2014 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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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

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Published: 07 April 2014

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WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

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WWW '14 Paper Acceptance Rate 84 of 645 submissions, 13%;

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

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Cited By

Fu ZNiu XMaher M(2023)Deep Learning Models for Serendipity Recommendations: A Survey and New PerspectivesACM Computing Surveys10.1145/360514556:1(1-26)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3605145
Hazrati NRicci F(2023)Choice models and recommender systems effects on users’ choicesUser Modeling and User-Adapted Interaction10.1007/s11257-023-09366-x34:1(109-145)Online publication date: 18-May-2023
https://doi.org/10.1007/s11257-023-09366-x
Doan TSahebi S(2020)TransCrossCF: Transition-based Cross-Domain Collaborative Filtering2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA51294.2020.00059(320-327)Online publication date: Dec-2020
https://doi.org/10.1109/ICMLA51294.2020.00059
Zhuang FZhou YYing HZhang FAo XXie XHe QXiong H(2020)Sequential Recommendation via Cross-Domain Novelty Seeking Trait MiningJournal of Computer Science and Technology10.1007/s11390-020-9945-z35:2(305-319)Online publication date: 27-Mar-2020
https://doi.org/10.1007/s11390-020-9945-z
Chong WLim E(2019)Fine-grained Geolocation of Tweets in Temporal ProximityACM Transactions on Information Systems10.1145/329105937:2(1-33)Online publication date: 11-Jan-2019
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Shi SZhang MLiu YMa SCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)Attention-based Adaptive Model to Unify Warm and Cold Starts RecommendationProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271710(127-136)Online publication date: 17-Oct-2018
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Chong WLim E(2018)Exploiting User and Venue Characteristics for Fine-Grained Tweet GeolocationACM Transactions on Information Systems10.1145/315666736:3(1-34)Online publication date: 2-Feb-2018
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Yi FFeng GWang HLi ZSun L(2018)MIACInternational Journal of Intelligent Systems in Accounting and Finance Management10.1002/isaf.143225:4(161-173)Online publication date: 16-Dec-2018
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