research-article

Addressing cold-start problem in recommendation systems

Authors:

Anh Duc DuongAuthors Info & Claims

ICUIMC '08: Proceedings of the 2nd international conference on Ubiquitous information management and communication

Pages 208 - 211

https://doi.org/10.1145/1352793.1352837

Published: 31 January 2008 Publication History

Abstract

Recommender systems for automatically suggested items of interest to users have become increasingly essential in fields where mass personalization is highly valued. The popular core techniques of such systems are collaborative filtering, content-based filtering and combinations of these. In this paper, we discuss hybrid approaches, using collaborative and also content data to address cold-start - that is, giving recommendations to novel users who have no preference on any items, or recommending items that no user of the community has seen yet. While there have been lots of studies on solving the item-side problems, solution for user-side problems has not been seen public. So we develop a hybrid model based on the analysis of two probabilistic aspect models using pure collaborative filtering to combine with users' information. The experiments with MovieLen data indicate substantial and consistent improvements of this model in overcoming the cold-start user-side problem.

References

[1]

M. Benjamin, "Collaborative Filtering - A Machine Learning Perspective," University of Torronto, 2004.

[2]

D. Billsus and M. J. Pazzani, "Learning collaborative information filters," Proceedings of the Fifteenth International Conference on Machine Learning, pp. 46--54, 1998.

Digital Library

[3]

J. S. Breese, D. Heckerman, and C. Kadie, "Empirical analysis of predictive algorithms for collaborative filtering," Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 34--52, 1998.

Digital Library

[4]

W. W. Cohen, R. E. Schapire, and Y. Singer, "Learning to order things," Journal of Artificial Intelligence Research, 243--270, 1999.

Digital Library

[5]

GroupLens Research, www.grouplens.org

[6]

J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl, "An algorithmic framework for performing collaborative filtering," Proceedings of the Conference on Research and Development in Information Retrieval, 1999.

Digital Library

[7]

T. Hofmann, "Probabilistic latent semantic analysis," Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, 1999.

Digital Library

[8]

T. Hofmann and J. Puzicha, "Latent class models for collaborative filtering," Proceedings of the Sixteenth International Join Conference on Artificial Intelligence, pp. 688--693, 1999.

Digital Library

[9]

R. M. Neal and G. E. Hinton, "A new view of the EM algorithm that justifies incremental, sparse and other variants," Learning in Graphical Models, pp. 355--368, Kluwer Academic Publishers, 1998.

Digital Library

[10]

D. M. Pennock, E. Horwitz, and C. L. Giles, "Social choice theory and recommender systems: Analysis of the axiomatic foundations of collaborative filtering," Proceedings of the Seventeenth National Conference on Artificial Intelligence, pp. 729--734, 2000.

Digital Library

[11]

D. M. Pennock, E. Horvitz, S. Lawrence, and C. L. Giles, "Collaborative filtering by personality diagnosis: A hybrid memory - and model-based approach," Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, pp. 473--480, 2000.

Digital Library

[12]

A. Popescul, L. H. Ungar, D. M. Pennock, and S. Lawrence, "Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments," Proceedings of the Seventeenth Conference on Uncertainty in Aritificial Intelligence, 2001.

Digital Library

[13]

B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedle, "Application of dimensionality reduction in recommender system - A case study," ACM WebKDD Web mining for E-Commerce Workshop, 2000.

[14]

B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedle, "Item-based collaborative filtering recommendation algorithms," Proceedings of the Tenth International World Wide Web Conference, pp. 285--295, 2001

Digital Library

[15]

H. Thomas, "Learning What People (Don't) Want," Proceedings of the European Conference on Machine Learning (ECML), 2001.

Digital Library

[16]

L. H. Ungar and D. P. Foster, "Clustering methods for collaborative filtering," Workshop on Recommendation Systems at the Fifteenth National Conference on Artificial Intelligence, 1998.

[17]

R. Jin, L. Si, and C. Zhai, "A Study of Mixture Models for Collaborative Filtering," Journal of Information Retrieval, 2006.

Digital Library

[18]

S. Park, D. Pennock, O. Madani, N. Good and D. DeCoste, "Naive filterbots for Robust Cold-start Recommendations," KDD'06, August 20--23, 2006

Digital Library

[19]

J. Kleinberg and M. Sandler, "Using Mixture Models for Collaborative Filtering," STOC'04, June 13--15, 2004.

Digital Library

[20]

E. Savia, K. Puolamaki, J. Sinkkonen, and S. Kaski, "Two-way latent grouping model for user preference prediction," Uncertainty in Artificial Intelligence 21, F. Bacchus and T. Jaakkola, Eds. Corvallis, Oregon: AUAI Press, pp. 518--525, 2005.

Cited By

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Kim YChang T(2024)Deep Learning-Based Freight Recommendation System for Freight Brokerage PlatformSystems10.3390/systems1211047712:11(477)Online publication date: 7-Nov-2024
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Addressing cold-start problem in recommendation systems

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cover image ACM Conferences

ICUIMC '08: Proceedings of the 2nd international conference on Ubiquitous information management and communication

January 2008

604 pages

ISBN:9781595939937

DOI:10.1145/1352793

General Chairs:
Won Kim
Sungkyunkwan University
,
Hyung Jin Choi
Sungkyunkwan University

Copyright © 2008 ACM.

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Published: 31 January 2008

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ICUIMC08: The Second International Conference on Ubiquitous Information Management and Communication

January 31 - February 1, 2008

Suwon, Korea

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Overall Acceptance Rate 251 of 941 submissions, 27%

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Cited By

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Kim YChang T(2024)Deep Learning-Based Freight Recommendation System for Freight Brokerage PlatformSystems10.3390/systems1211047712:11(477)Online publication date: 7-Nov-2024
https://doi.org/10.3390/systems12110477
Kanopka KDomingue B(2024)A Position-Sensitive Mixture Item Response ModelJournal of Educational and Behavioral Statistics10.3102/10769986241289399Online publication date: 25-Nov-2024
https://doi.org/10.3102/10769986241289399
Martin CBoutilier CMeshi OSandholm TLarson K(2024)Model-free preference elicitationProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/387(3493-3503)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/387
Zeng JNakano YSakato T(2024)Implementation and Evaluation of Interviewer’s Response Generation Model Considering Semantic Content意味内容に基づくインタビュアー応答生成モデルの作成と評価Transactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.39-3_IDS6-A39:3(IDS6-A_1-15)Online publication date: 1-May-2024
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Asri BIgmoullan IQassimi S(2024)Integrating Active Learning Strategies in Model Based Recommender SystemsProceedings of the 7th International Conference on Networking, Intelligent Systems and Security10.1145/3659677.3659838(1-8)Online publication date: 18-Apr-2024
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Monteil JVaskovych VLu WMajumder Avan den Hengel A(2024)MARec: Metadata Alignment for cold-start RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688125(401-410)Online publication date: 8-Oct-2024
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Zhong DWang XXu ZXu JWang WSerra ESpezzano F(2024)Interaction-level Membership Inference Attack against Recommender Systems with Long-tailed DistributionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679804(3433-3442)Online publication date: 21-Oct-2024
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Xi YLiu WLin JChen BTang RZhang WYu YSerra ESpezzano F(2024)MemoCRS: Memory-enhanced Sequential Conversational Recommender Systems with Large Language ModelsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679599(2585-2595)Online publication date: 21-Oct-2024
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