research-article

Enhancing recommender systems under volatile userinterest drifts

Authors:

Hui XiongAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 1257 - 1266

https://doi.org/10.1145/1645953.1646112

Published: 02 November 2009 Publication History

Abstract

This paper presents a systematic study of how to enhance recommender systems under volatile user interest drifts. A key development challenge along this line is how to track user interests dynamically. To this end, we first define four types of interest patterns to understand users' rating behaviors and analyze the properties of these patterns. We also propose a rating graph and rating chain based approach for detecting these interest patterns. For each users' rating series, a rating graph and a rating chain are constructed based on the similarities between rated items. The type of a given user's interest pattern is identified through the density of the corresponding rating graph and the continuity of the corresponding rating chain. In addition, we propose a general algorithm framework for improving recommender systems by exploiting these identified patterns. Finally, experimental results on a real-world data set show that the proposed rating graph based approach is effective for detecting user interest patterns, which in turn help to improve the performance of recommender systems.

References

[1]

http://www.grouplens.org/node/73#attachments.

[2]

Adomavicius, G., Alexander, and Tuzhilin, Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. In IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pages: 734--749, 2005.

Digital Library

[3]

Ahn, H. J., A new similarity measure for collaborative filtering to alleviate the new user cold starting problem. In InformationScience, vol. 178, no. 1, pages: 37--51, 2008.

Digital Library

[4]

Billsus, D., Brunk, C. A., Evans, C., Gladish, B., and Pazzani, M.,Adaptive interfaces for ubiquitous web access. In Comunications of the ACM, vol. 45, no. 5, pages: 34--38,2002.

Digital Library

[5]

Chen, J., Yin, J., and Huang, J., Automatic content-based recommendation in e-Commerce. In Proceedings of the 2005IEEE international Conference on e-Technology, e-Commerce and e-Service, pages: 748--753, 2005.

Digital Library

[6]

Debnath, S., Ganguly, N, and Mitra, P., Feature weighting in content based recommendation systems using social network analysis. In Proceedings of the 17th international conference on World Wide Web, Beijing, China, pages: 1041--1042, 2008.

Digital Library

[7]

Deshpande, Mukund and Karypis, George, Item-based top-N recommendation algorithms. In ACM Transactions on Information Systems, vol. 22, no. 1, pages: 143--177, 2004.

Digital Library

[8]

Ding, Yi, and Li, Xue, Time Weight Collaborative Filtering. In Proceedings of the 14th ACM international conference on Information and knowledge management, Bremen, Germany, pages:485--492, 2005.

Digital Library

[9]

Fan, W., Systematic data selection to mine concept-drifting data streams, In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining,Seattle, WA, USA, pages: 128--137, 2004.

Digital Library

[10]

Gori, Marco, and Pucci, Augusto, A random walk based scoring algorithm with application to recommender systems for large scale E-commerce. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,pages: 127--146, 2006.

[11]

Lam, Wai, Mostafa, and Javed, Modeling user interest shift using a bayesian approach. In Journal of the American Society for Information Science and Technology, vol. 52, no. 5, pages: 416--429,2001.

Digital Library

[12]

Linden, G., Smith, B., and York, J., Amazon.com recommendations:item-to-item collaborative filtering. In IEEE Internet Computing, vol. 7, no. 1, pages: 76--80, 2003.

Digital Library

[13]

Melville, P., Mooney, R.J., and Nagarajan, R., Content-boosted collaborative filtering for improved recommendations. In Proceedings of the 18th National Conference on Artificial Intelligence, Edmonton, Alberta, Canada, pages: 187--192, 2002.

Digital Library

[14]

Miller, B. N., Albert, I., Lam, S. K., Konstan, J. A., and Riedl,J., MovieLens unplugged: experiences with an occasionally connected recommender system. In Proceedings of the 8th International Conference on Intelligent User Interfaces,Miami, Florida, USA,pages: 263--266, 2003.

Digital Library

[15]

Min, Sung-Hwan, and Han, Ingoo, Detection of the customer time-variant pattern for improving recommender systems. In Expert Systems with Applications, vol. 28, no. 2, pages:189--199, 2005.

Digital Library

[16]

Mobasher, B., Dai, H., Luo, T., and Nakagawa, M., Improving the effectiveness of collaborative filtering on anonymous web usage Data. In Proceedings of IJCAI 2001 Workshop on Intelligent Techniques for Web Personalization, pages: 53--60, 2001.

[17]

Pedro, Bernaola-Galván, and Plamen, Ch. Ivanov, and Luís A.Nunes Amaral, and H. Eugene Stanley, Scale invariance in the nonstationarity of human heart rate. In Phys. Rev. Lett.,vol. 87, no. 16, pages: 168--105, 2001.

[18]

Resnick, P., and Varian, H., Recommender systems. In Communications of the ACM, vol. 40, no. 3, pages: 56--58,1997.

Digital Library

[19]

Sarwar, B., Karypis, G., Konstan, J., and Riedl, John, Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, Hong Kong, China, pages: 285--295, 2001.

Digital Library

[20]

Sarwar, B. M., Karypis, G., Konstan, J., and Riedl, J., Recommender systems for large-scale e-commerce: scalable neighborhood formationusing clustering. In Fifth International Conference on Computer and Information Technology, 2002.

[21]

Song, H. S., Kim, J. K., and Kim, S. H., Mining the change of customer behavior in an internet shopping mall. In Expert Systems with Applications, vol. 21, no. 3, pages 157--168, 2001.

[22]

Wang, H., Fan, W., Yu, P. S., and Han, J., Mining concept-drifting data streams using ensemble classifers. In Proceedings ofthe ninth ACM SIGKDD international conference on Knowledge discovery and data mining, Washington, D.C., USA, pages: 226--235, 2003.

Digital Library

[23]

Widyantoro, D. H., Ioerger, T. R., Yen, J., Tracking changes in user interests with a few relevance judgments. In Proceedings of the 12th international conference on Information and knowledge management, New Orleans, LA, USA, pages: 548--551, 2003.

Digital Library

[24]

Zhang, Y., Callan, J., and Minka, T., Novelty and redundancy detection in adaptive filtering. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, Tampere, Finland,pages:81--88, 2002.

Digital Library

[25]

Ziegler, C. N., McNee, S. M., Konstan, J. A., and Georg, Lausen, Improving recommendation lists through topic diversification. In Proceedings of the 14th international conference on World Wide Web, Chiba, Japan, pages: 22--32, 2005.

Digital Library

Cited By

Balcar SSkrhak VPeska L(2022)Rank-sensitive proportional aggregations in dynamic recommendation scenariosUser Modeling and User-Adapted Interaction10.1007/s11257-021-09311-w32:4(685-746)Online publication date: 1-Jan-2022
https://doi.org/10.1007/s11257-021-09311-w
Wang LZhang YZhu X(2021)Concept drift-aware temporal cloud service APIs recommendation for building composite cloud systemsJournal of Systems and Software10.1016/j.jss.2020.110902174(110902)Online publication date: Apr-2021
https://doi.org/10.1016/j.jss.2020.110902
Vassio LGaretto MChiasserini CLeonardi E(2020)User Interaction with Online AdvertisementsACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/33771445:2(1-26)Online publication date: 5-Mar-2020
https://dl.acm.org/doi/10.1145/3377144
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Index Terms

Enhancing recommender systems under volatile userinterest drifts
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image ACM Conferences

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

Copyright © 2009 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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Published: 02 November 2009

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November 2 - 6, 2009

Hong Kong, China

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Citations

Cited By

Balcar SSkrhak VPeska L(2022)Rank-sensitive proportional aggregations in dynamic recommendation scenariosUser Modeling and User-Adapted Interaction10.1007/s11257-021-09311-w32:4(685-746)Online publication date: 1-Jan-2022
https://doi.org/10.1007/s11257-021-09311-w
Wang LZhang YZhu X(2021)Concept drift-aware temporal cloud service APIs recommendation for building composite cloud systemsJournal of Systems and Software10.1016/j.jss.2020.110902174(110902)Online publication date: Apr-2021
https://doi.org/10.1016/j.jss.2020.110902
Vassio LGaretto MChiasserini CLeonardi E(2020)User Interaction with Online AdvertisementsACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/33771445:2(1-26)Online publication date: 5-Mar-2020
https://dl.acm.org/doi/10.1145/3377144
Jiang YYan SQi PSun Y(2020)Adapting to User Interest Drifts for Recommendations in Scratch2020 International Wireless Communications and Mobile Computing (IWCMC)10.1109/IWCMC48107.2020.9148105(1528-1534)Online publication date: Jun-2020
https://doi.org/10.1109/IWCMC48107.2020.9148105
Di Tommaso GPera SEkstrand MAmatriain XO'Donovan J(2018)SeRenAProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240327(558-562)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3240327
Wangwatcharakul CWongthanavasu S(2018)Improving Dynamic Recommender System Based on Item Clustering for Preference Drifts2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)10.1109/JCSSE.2018.8457395(1-6)Online publication date: Jul-2018
https://doi.org/10.1109/JCSSE.2018.8457395
Lommatzsch AKille BAlbayrak SSheth ANgonga AWang yChang EŚlęzak DFranczyk BAlt RTao X(2017)Incorporating context and trends in news recommender systemsProceedings of the International Conference on Web Intelligence10.1145/3106426.3109433(1062-1068)Online publication date: 23-Aug-2017
https://dl.acm.org/doi/10.1145/3106426.3109433
Faralli SStilo GVelardi P(2017)Automatic acquisition of a taxonomy of microblogs users interestsWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2017.05.00445:C(23-40)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1016/j.websem.2017.05.004
Liu H(2017)Resource recommendation via user tagging behavior analysisCluster Computing10.1007/s10586-017-1459-2Online publication date: 7-Dec-2017
https://doi.org/10.1007/s10586-017-1459-2
De Maio CFenza GGallo MLoia VParente M(2017)Social media marketing through time‐aware collaborative filteringConcurrency and Computation: Practice and Experience10.1002/cpe.409830:1Online publication date: 31-Mar-2017
https://doi.org/10.1002/cpe.4098
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