research-article

Collaborative topic modeling for recommending scientific articles

Authors:

David M. BleiAuthors Info & Claims

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 448 - 456

https://doi.org/10.1145/2020408.2020480

Published: 21 August 2011 Publication History

Abstract

Researchers have access to large online archives of scientific articles. As a consequence, finding relevant papers has become more difficult. Newly formed online communities of researchers sharing citations provides a new way to solve this problem. In this paper, we develop an algorithm to recommend scientific articles to users of an online community. Our approach combines the merits of traditional collaborative filtering and probabilistic topic modeling. It provides an interpretable latent structure for users and items, and can form recommendations about both existing and newly published articles. We study a large subset of data from CiteULike, a bibliography sharing service, and show that our algorithm provides a more effective recommender system than traditional collaborative filtering.

References

[1]

D. Agarwal and B.-C. Chen. Regression-based latent factor models. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 19--28, New York, NY, USA, 2009. ACM.

Digital Library

[2]

D. Agarwal and B.-C. Chen. flda: matrix factorization through latent Dirichlet allocation. In Proceedings of the third ACM international conference on Web search and data mining, WSDM '10, pages 91--100, New York, NY, USA, 2010. ACM.

Digital Library

[3]

D. Bertsekas. Nonlinear Programming. Athena Scientific, 1999.

[4]

D. Blei and J. Lafferty. A correlated topic model of Science. Annals of Applied Statistics, 1(1):17--35, 2007.

[5]

D. Blei and J. Lafferty. Topic models. In A. Srivastava and M. Sahami, editors, Text Mining: Theory and Applications. Taylor and Francis, 2009.

[6]

D. Blei and J. McAuliffe. Supervised topic models. In Neural Information Processing Systems, 2007.

[7]

D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993--1022, January 2003.

Digital Library

[8]

J. Chang, J. Boyd-Graber, S. Gerrish, C. Wang, and D. Blei. Reading tea leaves: How humans interpret topic models. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, editors, Advances in Neural Information Processing Systems 22, pages 288--296, 2009.

[9]

A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39:1--38, 1977.

[10]

S. M. Gerrish and D. M. Blei. Predicting legislative roll calls from text. In Proceedings of the 28th Annual International Conference on Machine Learning, ICML '11, 2011.

[11]

J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '99, pages 230--237, New York, NY, USA, 1999. ACM.

Digital Library

[12]

Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, pages 263--272, Washington, DC, USA, 2008. IEEE Computer Society.

Digital Library

[13]

Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, 42(8):30--37, 2009.

Digital Library

[14]

P. Melville, M. R., and R. Nagaraja. Content-boosted collaborative filtering for improved recommendations. In American Association for Artificial Intelligence, pages 187--192, 2002.

Digital Library

[15]

R. J. Mooney and L. Roy. Content-based book recommending using learning for text categorization. In Proceedings of the fifth ACM conference on Digital libraries, pages 195--204, New York, NY, USA, 2000. ACM.

Digital Library

[16]

R. Pan, Y. Zhou, B. Cao, N. N. Liu, R. Lukose, M. Scholz, and Q. Yang. One-class collaborative filtering. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, pages 502--511, Washington, DC, USA, 2008. IEEE Computer Society.

Digital Library

[17]

R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In Proceedings of the 25th International Conference on Machine learning, pages 880--887. ACM, 2008.

Digital Library

[18]

R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. Advances in Neural Information Processing Systems, 20:1257--1264, 2008.

Digital Library

[19]

H. Shan and A. Banerjee. Generalized probabilistic matrix factorizations for collaborative filtering. In Proceedings of the 2010 IEEE International Conference on Data Mining, pages 1025--1030, Washington, DC, USA, 2010. IEEE Computer Society.

Digital Library

[20]

Y. Teh, M. Jordan, M. Beal, and D. Blei. Hierarchical Dirichlet processes. Journal of the American Statistical Association, 101(476):1566--1581, 2007.

[21]

E. Wang, D. Liu, J. Silva, D. Dunson, and L. Carin. Joint analysis of time-evolving binary matrices and associated documents. In J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. Zemel, and A. Culotta, editors, Advances in Neural Information Processing Systems 23, pages 2370--2378. 2010.

[22]

K. Yu, J. Lafferty, S. Zhu, and Y. Gong. Large-scale collaborative prediction using a nonparametric random effects model. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 1185--1192, New York, NY, USA, 2009. ACM.

Digital Library

Cited By

Çiçeklioğlu A(2024)HABERE ÇEVRİM İÇİ ERİŞİMDE YENİ DÖNEM: KİŞİSELLEŞTİRİLMİŞ HABER UYGULAMALARIKritik İletişim Çalışmaları Dergisi10.53281/kritik.14383066:1(99-130)Online publication date: 2-Jul-2024
https://doi.org/10.53281/kritik.1438306
Guo YZhou Z(2024)SSRES: A Student Academic Paper Social Recommendation Model Based on a Heterogeneous Graph ApproachMathematics10.3390/math1211166712:11(1667)Online publication date: 27-May-2024
https://doi.org/10.3390/math12111667
Huang WLiu BWang Z(2024)A Metric Learning Perspective on the Implicit Feedback-Based Recommendation Data Imbalance ProblemElectronics10.3390/electronics1302041913:2(419)Online publication date: 19-Jan-2024
https://doi.org/10.3390/electronics13020419
Show More Cited By

Index Terms

Collaborative topic modeling for recommending scientific articles
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

Recommendations

Improving Collaborative Filtering Based Recommenders Using Topic Modelling
WI-IAT '14: Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 01

Standard Collaborative Filtering (CF) algorithms make use of interactions between users and items in the form of implicit or explicit ratings alone for generating recommendations. Similarity among users or items is calculated purely based on rating ...
Collaborative topic regression for online recommender systems: an online and Bayesian approach

Collaborative Topic Regression (CTR) combines ideas of probabilistic matrix factorization (PMF) and topic modeling (such as LDA) for recommender systems, which has gained increasing success in many applications. Despite enjoying many advantages, the ...
A hybrid recommendation technique using topic embedding for rating prediction and to handle cold-start problem
Abstract
Recommender systems aim to estimate item ratings and recommend items based on the users’ interests. The traditional recommender systems generally consider user–item rating information for rating prediction, but they suffer from various ...
Highlights
- A novel recommendation approach to handle the cold-start problem.
- Incorporating ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2011

1446 pages

ISBN:9781450308137

DOI:10.1145/2020408

General Chair:
Chid Apte
IBM Research
,
Program Chairs:
Joydeep Ghosh
UT Austin
,
Padhraic Smyth
UC Irvine

Copyright © 2011 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 August 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

KDD '11

Sponsor:

KDD '11: The 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2011

California, San Diego, USA

Acceptance Rates

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '25

Sponsor:
sigkdd
sigkdd

The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1,077
Total Citations
View Citations
7,603
Total Downloads

Downloads (Last 12 months)276
Downloads (Last 6 weeks)40

Reflects downloads up to 24 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Çiçeklioğlu A(2024)HABERE ÇEVRİM İÇİ ERİŞİMDE YENİ DÖNEM: KİŞİSELLEŞTİRİLMİŞ HABER UYGULAMALARIKritik İletişim Çalışmaları Dergisi10.53281/kritik.14383066:1(99-130)Online publication date: 2-Jul-2024
https://doi.org/10.53281/kritik.1438306
Guo YZhou Z(2024)SSRES: A Student Academic Paper Social Recommendation Model Based on a Heterogeneous Graph ApproachMathematics10.3390/math1211166712:11(1667)Online publication date: 27-May-2024
https://doi.org/10.3390/math12111667
Huang WLiu BWang Z(2024)A Metric Learning Perspective on the Implicit Feedback-Based Recommendation Data Imbalance ProblemElectronics10.3390/electronics1302041913:2(419)Online publication date: 19-Jan-2024
https://doi.org/10.3390/electronics13020419
Joshi SVashistha KBaliyan ASwati .(2024)Revolutionising E-commerce: Personalised Recommendations for Enhancing User EngagementSSRN Electronic Journal10.2139/ssrn.4823773Online publication date: 2024
https://doi.org/10.2139/ssrn.4823773
Bakhsh PIsmail MKhan MAli MMemon R(2024)Optimisation of Sentiment Analysis for E-CommerceVFAST Transactions on Software Engineering10.21015/vtse.v12i3.190712:3(243-262)Online publication date: 30-Sep-2024
https://doi.org/10.21015/vtse.v12i3.1907
刘世(2024)Multi-Granularity Attention Mechanism for Recommendation Systems Based on Item Descriptions and ReviewsModeling and Simulation10.12677/mos.2024.13322213:03(2429-2440)Online publication date: 2024
https://doi.org/10.12677/mos.2024.133222
Liang SPan Zliu wYin Jde Rijke M(2024)A Survey on Variational Autoencoders in Recommender SystemsACM Computing Surveys10.1145/366336456:10(1-40)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3663364
Che SLiu HLiu S(2024)Tagging Items with Emerging Tags: A Neural Topic Model Based Few-Shot Learning ApproachACM Transactions on Information Systems10.1145/364185942:4(1-37)Online publication date: 23-Jan-2024
https://dl.acm.org/doi/10.1145/3641859
Liu JChen CLee CHuang S(2024)Evolving Knowledge Graph Representation Learning with Multiple Attention Strategies for Citation Recommendation SystemACM Transactions on Intelligent Systems and Technology10.1145/363527315:2(1-26)Online publication date: 13-Jan-2024
https://dl.acm.org/doi/10.1145/3635273
Liu ZChen ZZhang MDuan SWen HLi LLi NGu YYu GChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Modeling User Viewing Flow using Large Language Models for Article RecommendationCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648305(83-92)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648305
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents