research-article

Recommender systems with non-binary grades

Authors:

Boaz Patt-ShamirAuthors Info & Claims

SPAA '11: Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures

Pages 245 - 252

https://doi.org/10.1145/1989493.1989528

Published: 04 June 2011 Publication History

Abstract

We consider the interactive model of recommender systems, in which users are asked about just a few of their preferences, and in return the system outputs an approximation of all their preferences. The measure of performance is the probe complexity of the algorithm, defined to be the maximal number of answers any user should provide (probe complexity typically depends inversely on the number of users with similar preferences and on the quality of the desired approximation). Previous interactive recommendation algorithms assume that user preferences are binary, meaning that each object is either "liked" or "disliked" by each user. In this paper we consider the general case in which users may have a more refined scale of preference, namely more than two possible grades. We show how to reduce the non-binary case to the binary one, proving the following results. For discrete grades with s possible values, we give a simple deterministic reduction that preserves the approximation properties of the binary algorithm at the cost of increasing probe complexity by factor s. Our main result is for the general case, where we assume that user grades are arbitrary real numbers. For this case we present an algorithm that preserves the approximation properties of the binary algorithm while incurring only polylogarithmic overhead.

References

[1]

N. Alon, B. Awerbuch, Y. Azar, and B. Patt-Shamir. Tell me who I am: an interactive recommendation system. In Proc. 18th Ann. ACM Symp. on Parallelism in Algorithms and Architectures (SPAA), pages 1--10, 2006.

Digital Library

[2]

B. Awerbuch, Y. Azar, Z. Lotker, B. Patt-Shamir, and M. Tuttle. Collaborate with strangers to find own preferences. In Proc. 17th ACM Symp. on Parallelism in Algorithms and Architectures (SPAA), pages 263--269, 2005.

Digital Library

[3]

B. Awerbuch, A. Nisgav, and B. Patt-Shamir. Asynchronous active recommendation systems. In Principles of distributed systems : 11th international conference (OPODIS 2007), volume 4878 of LNCS, pages 48--61, 2007.

Digital Library

[4]

B. Awerbuch, B. Patt-Shamir, D. Peleg, and M. Tuttle. Improved recommendation systems. In Proc. 16th Ann. ACM-SIAM Symp. on Discrete Algorithms (SODA), pages 1174--1183, 2005.

Digital Library

[5]

Y. Azar, A. Fiat, A. Karlin, F. McSherry, and J. Saia. Spectral analysis of data. In Proc. 33rd ACM Symp. on Theory of Computing (STOC), pages 619--626, 2001.

Digital Library

[6]

R. M. Bell and Y. Koren. Lessons from the netflix prize challenge. SIGKDD Explor. Newsl., 9(2):75--79, 2007.

Digital Library

[7]

P. Drineas, I. Kerenidis, and P. Raghavan. Competitive recommendation systems. In Proc. 34th ACM Symp. on Theory of Computing (STOC), pages 82--90, 2002.

Digital Library

[8]

S. Gilbert, R. Guerraoui, F. M. Rad, and M. Zadimoghaddam. Collaborative scoring with dishonest participants. In Proc. 22nd Ann. ACM Symp. on Parallel Algorithms and Architectures (SPAA), pages 41--49, 2010.

Digital Library

[9]

K. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval Journal, 4(2):133--151, July 2001.

Digital Library

[10]

J. Kleinberg and M. Sandler. Convergent algorithms for collaborative filtering. In Proc. 4th ACM Conf. on Electronic Commerce (EC), pages 1--10, 2003.

Digital Library

[11]

R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. Recommendation systems: A probabilistic analysis. In Proc. 39th IEEE Symp. on Foundations of Computer Science (FOCS), pages 664--673, 1998.

Digital Library

[12]

A. Nisgav and B. Patt-Shamir. Finding similar users in social networks: extended abstract. In Proc. 21st Ann. ACM Symp. on Parallelism in Algorithms and Architectures (SPAA), pages 169--177, 2009.

Digital Library

[13]

C. H. Papadimitriou, P. Raghavan, H. Tamaki, and S. Vempala. Latent semantic indexing: A probabilistic analysis. In Proc. 17th ACM Symp. on Principles of Database Systems (PODS), pages 159--168. ACM Press, 1998.

Digital Library

[14]

P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: an open architecture for collaborative filtering of netnews. In Proc. 1994 ACM Conf. on Computer Supported Cooperative Work, pages 175--186, Oct. 1994.

Digital Library

[15]

P. Resnick and H. R. Varian. Recommender systems. Commun. ACM, 40(3):56--58, 1997.

Digital Library

[16]

B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Analysis of recommendation algorithms for e-commerce. In Proc. 2nd ACM Conf. on Electronic Commerce (EC), pages 158--167. ACM Press, 2000.

Digital Library

Cited By

Carmel YPatt-Shamir B(2015)Comparison-Based Interactive Collaborative FilteringPost-Proceedings of the 22nd International Colloquium on Structural Information and Communication Complexity - Volume 943910.1007/978-3-319-25258-2_30(429-443)Online publication date: 14-Jul-2015
https://dl.acm.org/doi/10.1007/978-3-319-25258-2_30
Gao HChen DWang GMensah DFu Y(2014)A continuous rating model for news recommendationJournal of Information Science10.1177/016555151454206540:5(568-577)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1177/0165551514542065
Nisgav APatt-Shamir B(2011)Improved collaborative filteringProceedings of the 22nd international conference on Algorithms and Computation10.1007/978-3-642-25591-5_44(425-434)Online publication date: 5-Dec-2011
https://dl.acm.org/doi/10.1007/978-3-642-25591-5_44

Index Terms

Recommender systems with non-binary grades
1. Theory of computation
  1. Design and analysis of algorithms

Recommendations

A Scalable, Accurate Hybrid Recommender System
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining

Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...
Improving Accuracy of Recommender System by Item Clustering

Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches ...
A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems
UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

Much of the focus of recommender systems research has been on the accurate prediction of users' ratings for unseen items. Recent work has suggested that objectives such as diversity and novelty in recommendations are also important factors in the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SPAA '11: Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures

June 2011

404 pages

ISBN:9781450307437

DOI:10.1145/1989493

Co-chairs:
Friedhelm Meyer auf der Heide
University of Paderborn, Germany
,
Rajmohan Rajaraman
Northeastern University, USA

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

In-Cooperation

EATCS: European Association for Theoretical Computer Science

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 June 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SPAA '11

Sponsor:

SPAA '11: 23rd ACM Symposium on Parallelism in Algorithms and Architectures

June 4 - 6, 2011

California, San Jose, USA

Acceptance Rates

Overall Acceptance Rate 447 of 1,461 submissions, 31%

Upcoming Conference

SPAA '25

Sponsor:
sigact
sigact

37th ACM Symposium on Parallelism in Algorithms and Architectures

July 28 - August 1, 2025

Portland , OR , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
176
Total Downloads

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

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Carmel YPatt-Shamir B(2015)Comparison-Based Interactive Collaborative FilteringPost-Proceedings of the 22nd International Colloquium on Structural Information and Communication Complexity - Volume 943910.1007/978-3-319-25258-2_30(429-443)Online publication date: 14-Jul-2015
https://dl.acm.org/doi/10.1007/978-3-319-25258-2_30
Gao HChen DWang GMensah DFu Y(2014)A continuous rating model for news recommendationJournal of Information Science10.1177/016555151454206540:5(568-577)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1177/0165551514542065
Nisgav APatt-Shamir B(2011)Improved collaborative filteringProceedings of the 22nd international conference on Algorithms and Computation10.1007/978-3-642-25591-5_44(425-434)Online publication date: 5-Dec-2011
https://dl.acm.org/doi/10.1007/978-3-642-25591-5_44

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.

Figures

Tables

Media

View Table of Conten