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Exploiting Geo-Spatial Preference for Personalized Expert Recommendation

Authors:

James CaverleeAuthors Info & Claims

RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

Pages 67 - 74

https://doi.org/10.1145/2792838.2800189

Published: 16 September 2015 Publication History

Abstract

Experts are important for providing reliable and authoritative information and opinion, as well as for improving online reviews and services. While considerable previous research has focused on finding topical experts with broad appeal -- e.g., top Java developers, best lawyers in Texas -- we tackle the problem of personalized expert recommendation, to identify experts who have special personal appeal and importance to users. One of the key insights motivating our approach is to leverage the geo-spatial preferences of users and the variation of these preferences across different regions, topics, and social communities. Through a fine-grained GPS-tagged social media trace, we characterize these geo-spatial preferences for personalized experts, and integrate these preferences into a matrix factorization-based personalized expert recommender. Through extensive experiments, we find that the proposed approach can improve the quality of recommendation by 24% in precision compared to several baselines. We also find that users' geo-spatial preference of expertise and their underlying social communities can ameliorate the cold start problem by more than 20% in precision and recall.

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References

[1]

X. Amatriain, N. Lathia, J. M. Pujol, H. Kwak, and N. Oliver. The wisdom of the few: A collaborative filtering approach based on expert opinions from the web. In SIGIR, 2009.

Digital Library

[2]

K. Balog, L. Azzopardi, and M. De Rijke. Formal models for expert finding in enterprise corpora. In SIGIR, 2006.

Digital Library

[3]

C. Campbell, P. Maglio, A. Cozzi, and B. Dom. Expertise identification using email communications. In CIKM, 2003.

Digital Library

[4]

K. Chen, T. Chen, G. Zheng, Q. Jin, E. Yao, and Y. Yu. Collaborative personalized tweet recommendation. In SIGIR, 2012.

Digital Library

[5]

C. Cheng, H. Yang, I. King, and M. R. Lyu. Fused matrix factorization with geographical and social influence in location-based social networks. In AAAI, 2012.

Digital Library

[6]

Z. Cheng, J. Caverlee, H. Barthwal, and V. Bachani. Who is the barbecue king of texas?: A geo-spatial approach to finding local experts on twitter. In SIGIR, 2014.

Digital Library

[7]

S. Ghosh, N. Sharma, F. Benevenuto, N. Ganguly, and K. Gummadi. Cognos: Crowdsourcing search for topic experts in microblogs. In SIGIR, 2012.

Digital Library

[8]

Google. Overview of local guides, April 2015.

[9]

L. Hu, A. Sun, and Y. Liu. Your neighbors affect your ratings: On geographical neighborhood influence to rating prediction. In SIGIR, 2014.

Digital Library

[10]

Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 2009.

Digital Library

[11]

A. Krohn-Grimberghe, L. Drumond, C. Freudenthaler, and L. Schmidt-Thieme. Multi-relational matrix factorization using bayesian personalized ranking for social network data. In WSDM, 2012.

Digital Library

[12]

D. Lian, C. Zhao, X. Xie, G. Sun, E. Chen, and Y. Rui. Geomf: joint geographical modeling and matrix factorization for point-of-interest recommendation. In SIGKDD, 2014.

Digital Library

[13]

B. Liu, Y. Fu, Z. Yao, and H. Xiong. Learning geographical preferences for point-of-interest recommendation. In SIGKDD, 2013.

Digital Library

[14]

X. Liu, B. Croft, and M. Koll. Finding experts in community based question-answering services. In CIKM, 2005.

Digital Library

[15]

Y. Liu, W. Wei, A. Sun, and C. Miao. Exploiting geographical neighborhood characteristics for location recommendation. In CIKM, 2014.

Digital Library

[16]

H. Ma, D. Zhou, C. Liu, M. Lyu, and I. King. Recommender systems with social regularization. In WSDM, 2011.

Digital Library

[17]

A. Pal and S. Counts. Identifying topical authorities in microblogs. In WSDM, 2011.

Digital Library

[18]

S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In UAI, 2009.

Digital Library

[19]

S. Rendle and L. Schmidt-Thieme. Pairwise interaction tensor factorization for personalized tag recommendation. In WSDM, 2010.

Digital Library

[20]

J. Weng, E.P. Lim, J. Jiang, and Q. He. Twitterrank: Finding topic-sensitive influential twitterers. In WSDM, 2010.

Digital Library

[21]

M. Ye, P. Yin, W.C. Lee, and D.L. Lee. Exploiting geographical influence for collaborative point-of-interest recommendation. In SIGIR, 2011.

Digital Library

[22]

J. Zhang, M. S. Ackerman, and L. Adamic. Expertise networks in online communities: structure and algorithms. In WWW, 2007.

Digital Library

[23]

W. Zhang, J. Wang, and W. Feng. Combining latent factor model with location features for event-based group recommendation. In SIGKDD, 2013.

Digital Library

Cited By

Fan XJi YHui B(2024)A dynamic preference recommendation model based on spatiotemporal knowledge graphsComplex & Intelligent Systems10.1007/s40747-024-01658-y11:1Online publication date: 18-Nov-2024
https://doi.org/10.1007/s40747-024-01658-y
Aftab SRamampiaro HLangseth HRuocco M(2023)Deep Contextual Grid Triplet Network for Context-Aware RecommendationIEEE Access10.1109/ACCESS.2023.331047011(97522-97537)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3310470
Chen PLi J(2019)Weighted sequence loss based recurrent model for repurchase recommendationIOP Conference Series: Materials Science and Engineering10.1088/1757-899X/490/6/062062490(062062)Online publication date: 15-Apr-2019
https://doi.org/10.1088/1757-899X/490/6/062062
Show More Cited By

Index Terms

Exploiting Geo-Spatial Preference for Personalized Expert Recommendation
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

September 2015

414 pages

ISBN:9781450336925

DOI:10.1145/2792838

General Chairs:
Hannes Werthner
TU Wien, Austria
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria
,
Program Chairs:
Jennifer Golbeck
University of Maryland, USA
,
Giovanni Semeraro
University of Bari Aldo Moro, Italy

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 September 2015

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NSF
Google Research Award

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RecSys '15

Sponsor:

SIGCHI

RecSys '15: Ninth ACM Conference on Recommender Systems

September 16 - 20, 2015

Vienna, Austria

Acceptance Rates

RecSys '15 Paper Acceptance Rate 28 of 131 submissions, 21%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Fan XJi YHui B(2024)A dynamic preference recommendation model based on spatiotemporal knowledge graphsComplex & Intelligent Systems10.1007/s40747-024-01658-y11:1Online publication date: 18-Nov-2024
https://doi.org/10.1007/s40747-024-01658-y
Aftab SRamampiaro HLangseth HRuocco M(2023)Deep Contextual Grid Triplet Network for Context-Aware RecommendationIEEE Access10.1109/ACCESS.2023.331047011(97522-97537)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3310470
Chen PLi J(2019)Weighted sequence loss based recurrent model for repurchase recommendationIOP Conference Series: Materials Science and Engineering10.1088/1757-899X/490/6/062062490(062062)Online publication date: 15-Apr-2019
https://doi.org/10.1088/1757-899X/490/6/062062
Cao JYang YCao BXue LLi SIqbal MMumtaz S(2019)Local experts finding using user comments in location‐based social networksTransactions on Emerging Telecommunications Technologies10.1002/ett.360030:9Online publication date: 12-Sep-2019
https://dl.acm.org/doi/10.1002/ett.3600
Cacheda FBarbieri NTramullas JTrillo-Lado RNogueras-Iso J(2018)Click-through prediction when searching local businessesProceedings of the 5th Spanish Conference on Information Retrieval10.1145/3230599.3230609(1-2)Online publication date: 26-Jun-2018
https://dl.acm.org/doi/10.1145/3230599.3230609
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
Beutel ACovington PJain SXu CLi JGatto VChi EChang YZhai CLiu YMaarek Y(2018)Latent CrossProceedings of the Eleventh ACM International Conference on Web Search and Data Mining10.1145/3159652.3159727(46-54)Online publication date: 2-Feb-2018
https://dl.acm.org/doi/10.1145/3159652.3159727
Cacheda FBlanco RBarbieri N(2018)Characterizing and Predicting Users’ Behavior on Local Search QueriesACM Transactions on the Web10.1145/315705912:2(1-32)Online publication date: 27-May-2018
https://dl.acm.org/doi/10.1145/3157059
Ebrahimi MShafieiBavani EWong RChen F(2018)Twitter user geolocation by filtering of highly mentioned usersJournal of the Association for Information Science and Technology10.1002/asi.2401169:7(879-889)Online publication date: 22-Feb-2018
https://doi.org/10.1002/asi.24011
Cacheda FBarbieri NBlanco Rde Rijke MShokouhi MTomkins AZhang M(2017)Click Through Rate Prediction for Local Search ResultsProceedings of the Tenth ACM International Conference on Web Search and Data Mining10.1145/3018661.3018683(171-180)Online publication date: 2-Feb-2017
https://dl.acm.org/doi/10.1145/3018661.3018683
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