Article

Improving new user recommendations with rule-based induction on cold user data

Authors:

Nathalie Denos,

Catherine BerrutAuthors Info & Claims

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

Pages 121 - 128

https://doi.org/10.1145/1297231.1297251

Published: 19 October 2007 Publication History

Abstract

With recommender systems, users receive items recommended on the basis of their profile. New users experience the cold start problem: as their profile is very poor, the system performs very poorly. In this paper, classical new user cold start techniques are improved by exploiting the cold user data, i.e. the user data that is readily available (e.g. age, occupation, location, etc.), in order to automatically associate the new user with a better first profile. Relying on the existing α-community spaces model, a rule-based induction process is used and a recommendation process based on the "level of agreement" principle is defined. The experiments show that the quality of recommendations compares to that obtained after a classical new user technique, while the new user effort is smaller as no initial ratings are asked.

References

[1]

Breese J. S., Heckerman, D. and Kadie, C. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of the 14th Conference on Uncertainty In Artificial Intelligence (UAI'98), Madison, Wisconsin, USA, 43--52, July 1998.

Digital Library

[2]

Burke, R. Hybrid Recommender Systems: Survey and Experiments. User Modeling and User Adapted Interaction, 12, 4 (Nov. 2002), 331--370.

Digital Library

[3]

C5.0, Release 2.02, September 2005, http://www.rulequest.com/see5-info.html.

[4]

CBA v2.1, June 2001, http://www.comp.nus.edu.sg/~dm2/.

[5]

Claypool, M,. Gokhale, A. and Miranda, T. Combining Content-Based and Collaborative Filters. In Proceedings of the 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'99, Berkeley, CA, USA, 1999.

[6]

Dorigo, M., Bonabeau, E. and Theraulaz, G. Ant algorithms and stigmergy. In Future Generation Computer Systems (FGCS), vol. 16, Elsevier, 851--871, 2000.

Digital Library

[7]

Good, N., Schafer, J. B., Konstan, J. A., Borchers, A., Sarwar, B., Herlocker, J. and Riedl, J. Combining collaborative filtering with personal agents for better recommendations. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, 439--446, Orlando, USA, 1999.

Digital Library

[8]

Handl, J., Knowles, J., Dorigo, M. On the performance of ant-based clustering. In Proceedings of the 3rd International Conference on Hybrid Intelligence Systems, Australia, 2003.

[9]

Herlocker, J. L., Konstan, J. A., Terveen, L. G., Riedl, J. T. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22, 1 (Jan. 2004), 5--53.

Digital Library

[10]

Krulwich, B., Lifestyle Finder: Intelligent user profiling using large-scale demographic data. AI Magazine, 18, 2, 37--45, 1997.

[11]

Liu B., Ma Y. and Wong C-K., Improving an Association Rule Based Classifier, In Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD 2000), Lyon, France, Sept. 13--16, 2000, 504--509.

Digital Library

[12]

Maltz, D. and Ehrlich, E. Pointing the way: Active collaborative filtering. In Proceedings of the SIGCHI conference on Human factors in computing systems (CHI'95) (Denver, Colorado, USA). 1995, 202--209.

Digital Library

[13]

Melville, P., Mooney, R. J. and Nagarajan, R. Content-Boosted Collaborative Filtering for Improved Recommendations. In Proceedings of the Eighteenth National Conference on Artificial Intelligence, AAAI'02, Edmonton, Canada, 187--192, 2002.

Digital Library

[14]

Middleton, S. E., Alani, H., Shadbolt, N. R. and De Roure, D. C. Exploiting Synergy Between Ontologies and Recommender Systems. In Proceedings of the 11th International World Wide Web Conference WWW-2002, International Workshop on the Semantic Web, Hawaii, USA, 2002.

[15]

Middleton, S. E., Shadbolt, N. R. and De Roure, D. C. Ontological user profiling in recommender systems. ACM Transactions on Information Systems, 22, 1 (Jan. 2004) 54--88.

Digital Library

[16]

MovieLens dataset. http://www.grouplens.org/

[17]

MovieLens system. http://www.movielens.umn.edu/

[18]

Nguyen, A.-T. COCoFil2 : Un nouveau système de filtrage collaboratif basé sur le modèle des espaces de communautés. Ph.D. Thesis, University Joseph Fourier, Grenoble, France, 2006.

[19]

Nguyen, A.-T., Denos, N. and Berrut, C. Modèle d'espaces de communautés basé sur la théorie des ensembles d'approximation dans un système de filtrage hybride, In Actes de la 3ème COnférence en Recherche Information et Applications (CORIA'06), (Lyon, France, March 15-17, 2006), 2006, 303--314.

[20]

Pazzani, M., A framework for collaborative, content-based and demographic filtering, Artificial Intelligence Review, 13, 5, 393--408, 1999.

Digital Library

[21]

Pawlak Z. Rough Sets. International Journal of Computer and Information Sciences, 11, 5, 341--356, Plenum Publishing Corporation, 1982.

[22]

Pawlak Z., Skowron A. Rough membership functions. In Advances in the Dempster Shafer Theory of Evidence, John Wiley & Sons Inc., 251--271, 1994.

Digital Library

[23]

Quinlan, J. R., C4.5 : Programs for Machine Learning, Morgan Kaufmann, San Mateo, USA, 1993.

Digital Library

[24]

Rashid, A., Albert, I., Cosley, D., Lam, S. K., Mcnee, S. M., Konstan, J. A. and Riedl, J. Getting to Know You: Learning New User Preferences in Recommender Systems. In Proceedings of the 7th International Conference on Intelligent User Interfaces, IUI'02, San Francisco, California, USA, p. 127--134, 2002.

Digital Library

[25]

Schein, A. I., Popescul, A., Ungar, L. H., and Pennock, D. M. 2002. Methods and metrics for cold start recommendations. In Proceedings of the 25th Annual international ACM SIGIR Conference on Research and Development in information Retrieval (Tampere, Finland, August 11-15, 2002). SIGIR '02. ACM Press, New York, NY, 253--260.

Digital Library

[26]

Shafer G. A Mathematical Theory of Evidence. Princeton University Press, 1976.

[27]

Yao Y.-Y., A comparative study of fuzzy sets and rough sets. Information Sciences, 109, 1-4, 227--242, 1998.

[28]

Yao Y.-Y. Generalized rough set models. In Rough Sets in Knowledge Discovery, Polkowski L. and Skowron A. (Eds.), Physica-Verlag, Heidelberg, 286--318, 1998.

[29]

Zadeh L., Fuzzy Sets, Information and Control, 8 (3), 1965.

Cited By

Ahn YLin Y(2024)Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender SystemsACM Transactions on Intelligent Systems and Technology10.1145/365004415:4(1-20)Online publication date: 29-Feb-2024
https://dl.acm.org/doi/10.1145/3650044
Zhang GLiu YJin X(2022)A survey of autoencoder-based recommender systemsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-018-8052-614:2(430-450)Online publication date: 11-Mar-2022
https://dl.acm.org/doi/10.1007/s11704-018-8052-6
Zhu SShen WQu A(2022) Weighted AutoEncoding recommender system Statistical Analysis and Data Mining: The ASA Data Science Journal10.1002/sam.1157115:5(570-585)Online publication date: 7-Jan-2022
https://doi.org/10.1002/sam.11571
Show More Cited By

Index Terms

Improving new user recommendations with rule-based induction on cold user data
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
    2. Users and interactive retrieval
      1. Personalization
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

Recommendations

Naïve filterbots for robust cold-start recommendations
KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

The goal of a recommender system is to suggest items of interest to a user based on historical behavior of a community of users. Given detailed enough history, item-based collaborative filtering (CF) often performs as well or better than almost any ...
Cross-representation mediation of user models

Personalization is considered a powerful methodology for improving the effectiveness of information search and decision making. It has led to the dissemination of systems capable of suggesting relevant and personalized information (or items) to the users,...
User Similarity Adjustment for Improved Recommendations
MIKE 2015: Proceedings of the Third International Conference on Mining Intelligence and Knowledge Exploration - Volume 9468

Recommender systems are becoming more and more attractive in both research and commercial communities due to Information overload problem and the popularity of the Internet applications. Collaborative Filtering, a popular branch of recommendation ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

October 2007

222 pages

ISBN:9781595937308

DOI:10.1145/1297231

General Chair:
Joseph A. Konstan
University of Minnesota, USA
,
Program Chairs:
John Riedl
University of Minnesota, USA
,
Barry Smyth
University College, Dublin, Ireland

Copyright © 2007 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

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 October 2007

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

RecSys07

Sponsor:

RecSys07: ACM Conference on Recommender Systems

October 19 - 20, 2007

MN, Minneapolis, USA

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

35
Total Citations
View Citations
1,120
Total Downloads

Downloads (Last 12 months)20
Downloads (Last 6 weeks)1

Reflects downloads up to 17 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ahn YLin Y(2024)Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender SystemsACM Transactions on Intelligent Systems and Technology10.1145/365004415:4(1-20)Online publication date: 29-Feb-2024
https://dl.acm.org/doi/10.1145/3650044
Zhang GLiu YJin X(2022)A survey of autoencoder-based recommender systemsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-018-8052-614:2(430-450)Online publication date: 11-Mar-2022
https://dl.acm.org/doi/10.1007/s11704-018-8052-6
Zhu SShen WQu A(2022) Weighted AutoEncoding recommender system Statistical Analysis and Data Mining: The ASA Data Science Journal10.1002/sam.1157115:5(570-585)Online publication date: 7-Jan-2022
https://doi.org/10.1002/sam.11571
Mohammadi MNaree SLati M(2020)User-item content awareness in matrix factorization based collaborative recommender systemsIntelligent Data Analysis10.3233/IDA-19459924:3(723-739)Online publication date: 21-May-2020
https://doi.org/10.3233/IDA-194599
Shi SChen HMa WMao JZhang MZhang Yd'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Neural Logic ReasoningProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3411949(1365-1374)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3411949
Li JGuo XWu L(2020)A Hybrid Recommendation Algorithm Combing Naive Bayes Classifier and the Users’ Trust Relationship2020 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC)10.1109/IPEC49694.2020.9115132(129-134)Online publication date: Apr-2020
https://doi.org/10.1109/IPEC49694.2020.9115132
Tahmasebi FMeghdadi MAhmadian SValiallahi K(2020)A hybrid recommendation system based on profile expansion technique to alleviate cold start problemMultimedia Tools and Applications10.1007/s11042-020-09768-8Online publication date: 13-Sep-2020
https://doi.org/10.1007/s11042-020-09768-8
Hwangbo HKim Y(2019)Session-Based Recommender System for Sustainable Digital MarketingSustainability10.3390/su1112333611:12(3336)Online publication date: 17-Jun-2019
https://doi.org/10.3390/su11123336
Wang YBi XQu A(2019)A Logistic Factorization Model for Recommender Systems With Multinomial ResponsesJournal of Computational and Graphical Statistics10.1080/10618600.2019.166553529:2(396-404)Online publication date: 25-Oct-2019
https://doi.org/10.1080/10618600.2019.1665535
Arpetti JIovanella A(2019)Towards more effective consumer steering via network analysisEuropean Journal of Law and Economics10.1007/s10657-019-09637-250:3(359-380)Online publication date: 28-Nov-2019
https://doi.org/10.1007/s10657-019-09637-2
Show More Cited By

View Options

Get Access

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