research-article

Investigations into user rating information and predictive accuracy in a collaborative filtering domain

Authors:

Josephine Griffith,

Colm O'Riordan,

Humphrey SorensenAuthors Info & Claims

SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing

Pages 937 - 942

https://doi.org/10.1145/2245276.2245458

Published: 26 March 2012 Publication History

Abstract

The work described in this paper extracts user rating information from collaborative filtering datasets, and for each dataset uses a supervised machine learning approach to identify if there is an underlying relationship between rating information in the dataset and the expected accuracy of recommendations returned by the system. The underlying relationship is represented by decision tree rules. The rules can be used to indicate the predictive accuracy of the system for users of the system. Thus a user can know in advance of recommendation the level of accuracy to expect from the collaborative filtering system and may have more (or less) confidence in the recommendations produced. The experiment outlined in this paper aims to test the accuracy of the rules produced using three different datasets. Results show good accuracy can be found for all three datasets.

References

[1]

A. Bellogín and P. Castells. Predicting neighbor goodness in collaborative filtering. In 8th International Conference on Flexible Query Answering Systems, pages 605--616, 2009.

Digital Library

[2]

A. Bellogín and P. Castells. A performance prediction approach to enhance collaborative filtering performance. In 32nd European Conference on Information Retrieval (ECIR), pages 382--393, 2010.

Digital Library

[3]

J. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, July 1998.

Digital Library

[4]

S. Cronen-Townsend, Y. Zhou, and W. B. Croft. Predicting query performance. In 25th Annual International ACM conference on Research and Development in Information Retrieval (SIGIR), 2002.

Digital Library

[5]

J. Griffith, C. O'Riordan, and H. Sorensen. Using user model information to support collaborative filtering recommendations. In Proceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science, pages 71--80, 2007.

[6]

J. Herlocker, J. Konstan, L. G. Terveen, and J. Riedl. Evaluating collaborative filtering recomender systems. ACM Transactions on Information Systems, 22(1): 5--53, 2004.

Digital Library

[7]

R. Jin, J. Y. Chai, and L. Si. An automatic weighting scheme for collaborative filtering. In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR '04), pages 337--344, 2004.

Digital Library

[8]

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

Digital Library

[9]

H. Ma, I. King, and M. R. Lyu. Effective missing data prediction for collaborative filtering. In Annual International ACM conference on Research and Development in Information Retrieval (SIGIR), pages 39--46, 2007.

Digital Library

[10]

J. O'Donovan and B. Smyth. Trust in recommender systems. In Proceedings of the 10th international conference on Intelligent user interfaces, pages 167--174, 2005.

Digital Library

[11]

J. R. Quinlan. Learning with continuous classes. In Artificial Intelligence, pages 343--348, 1992.

[12]

A. M. Rashid, G. Karypis, and J. Riedl. Influence in ratings-based recommender systems: An algorithm-independent approach. In SIAM International Conference on Data Mining, 2005.

[13]

A. Saric, M. Hadzikadic, and D. Wilson. Alternative formulas for rating prediction using collaborative filtering. In Foundations of Intelligent Systems, Lecture Notes in Computer Science, pages 301--310, 2009.

Digital Library

[14]

I. H. Witten, E. Frank, and M. A. Hall. Data Mining Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 3rd edition, 2011.

Digital Library

[15]

E. Yom-Tov, S. Fine, D. Carmel, and A. Darlow. Learning to estimate query difficulty. In 28th Annual International ACM conference on Research and Development in Information Retrieval (SIGIR), 2005.

Digital Library

[16]

K. Yu, X. Xu, J. Tao, M. E. Kri, and H.-P. Kriegel. Feature weighting and instance selection for collaborative filtering: An information-theoretic approach. Knowledge and Information Systems, 5(2), 2003.

Digital Library

[17]

Y. Zhou and B. W. Croft. Ranking robustness: a novel framework to predict query performance. In 15th ACM conference on Information and Knowledge Management (CIKM), 2006.

Digital Library

Cited By

Wegmeth LVente TBeel J(2024)Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback DatasetsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691718(1163-1167)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691718
McElfresh DKhandagale SValverde JDickerson JWhite CKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)On the generalizability and predictability of recommender systemsProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3600589(4416-4432)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3600589
Varela DAguilar JMonsalve-Pulido JMontoya E(2022)Analysis of Meta-Features in the Context of Adaptive Hybrid Recommendation Systems2022 XVLIII Latin American Computer Conference (CLEI)10.1109/CLEI56649.2022.9959945(1-10)Online publication date: 17-Oct-2022
https://doi.org/10.1109/CLEI56649.2022.9959945
Show More Cited By

Index Terms

Investigations into user rating information and predictive accuracy in a collaborative filtering domain
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

Recommendations

A hybrid approach for improving predictive accuracy of collaborative filtering algorithms

Recommender systems represent a class of personalized systems that aim at predicting a user's interest on information items available in the application domain, operating upon user-driven ratings on items and/or item features. One of the most widely ...
Improvement of collaborative filtering using rating normalization

With the advent of the Internet, the types and amount of information one can access have increased dramatically. In today's overwhelming information environment, recommendation systems that quickly analyze large amounts of available information and help ...
Exploiting user interests for collaborative filtering: interests expansion via personalized ranking
CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

In real applications, a given user buys or rates an item based on his/her interests. Learning to leverage this interest information is often critical for recommender systems. However, in existing recommender systems, the information about latent user ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing

March 2012

2179 pages

ISBN:9781450308571

DOI:10.1145/2245276

Conference Chairs:
Sascha Ossowski
University Rey Juan Carlos, Spain
,
Paola Lecca
The Microsoft Research - University of Trento COSBI, Italy

Copyright © 2012 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

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 March 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SAC 2012

Sponsor:

SIGAPP

SAC 2012: ACM Symposium on Applied Computing

March 26 - 30, 2012

Trento, Italy

Acceptance Rates

SAC '12 Paper Acceptance Rate 270 of 1,056 submissions, 26%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
241
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)3

Reflects downloads up to 24 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wegmeth LVente TBeel J(2024)Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback DatasetsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691718(1163-1167)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691718
McElfresh DKhandagale SValverde JDickerson JWhite CKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)On the generalizability and predictability of recommender systemsProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3600589(4416-4432)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3600589
Varela DAguilar JMonsalve-Pulido JMontoya E(2022)Analysis of Meta-Features in the Context of Adaptive Hybrid Recommendation Systems2022 XVLIII Latin American Computer Conference (CLEI)10.1109/CLEI56649.2022.9959945(1-10)Online publication date: 17-Oct-2022
https://doi.org/10.1109/CLEI56649.2022.9959945
Cunha TSoares Cde Carvalho APera SEkstrand MAmatriain XO'Donovan J(2018)CF4CFProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240378(357-361)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3240378
Cunha TSoares Cde Carvalho AHaddad HWainwright RChbeir R(2018)A label ranking approach for selecting rankings of collaborative filtering algorithmsProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3167418(1393-1395)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3167132.3167418
Said ABellogín A(2018)Coherence and inconsistencies in rating behaviorUser Modeling and User-Adapted Interaction10.1007/s11257-018-9202-028:2(97-125)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11257-018-9202-0
Cunha TSoares Cde Carvalho A(2018)CF4CF-META: Hybrid Collaborative Filtering Algorithm Selection FrameworkDiscovery Science10.1007/978-3-030-01771-2_8(114-128)Online publication date: 7-Oct-2018
https://doi.org/10.1007/978-3-030-01771-2_8
Cunha TSoares CCarvalho ACremonesi PRicci FBerkovsky STuzhilin A(2017)Metalearning for Context-aware FilteringProceedings of the Eleventh ACM Conference on Recommender Systems10.1145/3109859.3109899(14-22)Online publication date: 27-Aug-2017
https://dl.acm.org/doi/10.1145/3109859.3109899
Lian TLi ZChen ZMa J(2017)The Impact of Profile Coherence on Recommendation Performance for Shared Accounts on Smart TVsInformation Retrieval10.1007/978-3-319-68699-8_3(30-41)Online publication date: 21-Oct-2017
https://doi.org/10.1007/978-3-319-68699-8_3
Cunha TSoares Cde Carvalho A(2017)Recommending Collaborative Filtering Algorithms Using Subsampling LandmarkersDiscovery Science10.1007/978-3-319-67786-6_14(189-203)Online publication date: 16-Sep-2017
https://doi.org/10.1007/978-3-319-67786-6_14
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