Article

Eigentaste 5.0: constant-time adaptability in a recommender system using item clustering

Authors:

Tavi Nathanson,

Ken GoldbergAuthors Info & Claims

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

Pages 149 - 152

https://doi.org/10.1145/1297231.1297258

Published: 19 October 2007 Publication History

Abstract

Recommender systems strive to recommend items that users will appreciate and rate highly, often presenting items in order of highest predicted ratings first. In this working paper we present Eigentaste 5.0, a constant-time recommender system that dynamically adapts the order that items are recommended by integrating user clustering with item clustering and monitoring item portfolio effects. This extends our Eigentaste 2.0 algorithm, which uses principal component analysis to cluster users offline. In preliminary experiments we backtested Eigentaste 5.0 on data collected from Jester, our online joke recommender system. Results suggest that it will perform better than Eigentaste 2.0. The new algorithm also uses item clusters to address the cold-start problem for introducing new items.

References

[1]

D. Cosley, S. Lam, I. Albert, J. Konstan, and J. Riedl. Is seeing believing?: how recommender system interfaces affect users' opinions. Proc. of the Annual SIGCHI Conf., pages 585--592, 2003.

Digital Library

[2]

M. Deshpande and G. Karypis. Item-based top-N recommendation algorithms. ACM TOIS, 22(1):143--177, 2004.

Digital Library

[3]

T. George and S. Merugu. A scalable collaborative filtering framework based on co-clustering. Proc. of the 5th IEEE Int'l Conf. on Data Mining, pages 625--628, 2005.

Digital Library

[4]

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

Digital Library

[5]

J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. ACM TOIS, 22(1):5--53, 2004.

Digital Library

[6]

D. Kim and B. Yum. Collaborative filtering based on iterative principal component analysis. Expert Systems with Applications, 28(4):823--830, 2005.

Digital Library

[7]

J. Konstan, S. McNee, C. Ziegler, R. Torres, N. Kapoor, and J. Riedl. Lessons on Applying Automated Recommender Systems to Information Seeking Tasks. Proceedings of the Twenty-First National Conference on Artificial Intelligence, 2006.

Digital Library

[8]

D. Lemire. Scale and translation invariant collaborative filtering systems. Information Retrieval, 8(1):129--150, 2005.

Digital Library

[9]

G. Linden, B. Smith, and J. York. Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing, 7(1):76--80, 2003.

Digital Library

[10]

S. Park, D. Pennock, O. Madani, N. Good, and D. DeCoste. Naïve filterbots for robust cold-start recommendations. Proc. of the 12th ACM SIGKDD Int'l Conf., pages 699--705, 2006.

Digital Library

[11]

D. Pennock, E. Horvitz, S. Lawrence, and C. Giles. Collaborative filtering by personality diagnosis: a hybrid memory and model-based approach. Proc. of the 16th Conf. on Uncertainty in Artificial Intelligence, pages 473--480, 2000.

Digital Library

[12]

T. Quan, I. Fuyuki, and H. Shinichi. Improving accuracy of recommender system by clustering items based on stability of user similarity. IAWTIC'2006 Proc., pages 61--61, 2006.

Digital Library

[13]

A. Rashid, I. Albert, D. Cosley, S. Lam, S. McNee, J. Konstan, and J. Riedl. Getting to know you: learning new user preferences in recommender systems. Proc. of the 7th Int'l Conf. on Intelligent User Interfaces, pages 127--134, 2002.

Digital Library

[14]

A. Rashid, S. Lam, G. Karypis, and J. Riedl. ClustKNN: a highly scalable hybrid model & memory-based CF algorithm. Proc. of WebKDD 2006, 2006.

[15]

A. Schein, A. Popescul, L. Ungar, and D. Pennock. Methods and metrics for cold-start recommendations. Proc. of the 25th Annual Int'l ACM SIGIR Conf., pages 253--260, 2002.

Digital Library

[16]

J. Thornton. Collaborative filtering research papers.

[17]

M. Vozalis and K. Margaritis. On the combination of user-based and item-based collaborative filtering. International Journal of Computer Mathematics, 81(9):1077--1096, 2004.

[18]

J. Wang, A. de Vries, and M. Reinders. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. Proc. of the 29th Annual Int'l ACM SIGIR Conf., pages 501--508, 2006.

Digital Library

[19]

D. Wilson, B. Smyth, and D. Sullivan. Sparsity reduction in collaborative recommendation: a casebased approach. Int'l Journal of Pattern Recognition and Artificial Intelligence, 17(5):863--884, 2003.

[20]

G. Xue, C. Lin, Q. Yang, W. Xi, H. Zeng, Y. Yu, and Z. Chen. Scalable collaborative filtering using cluster-based smoothing. Proc. of the 28th Annual Int'l ACM SIGIR Conf., pages 114--121, 2005.

Digital Library

[21]

C. Ziegler, S. McNee, J. Konstan, and G. Lausen. Improving recommendation lists through topic diversification. Proceedings of the 14th international conference on World Wide Web, pages 22--32, 2005.

Digital Library

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Index Terms

Eigentaste 5.0: constant-time adaptability in a recommender system using item clustering
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications
    1. Data mining
      1. Clustering

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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]

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Published: 19 October 2007

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RecSys07: ACM Conference on Recommender Systems

October 19 - 20, 2007

MN, Minneapolis, USA

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

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https://doi.org/10.1109/TFUZZ.2024.3389733
Li JLuo XYuan YGao S(2024)A Nonlinear PID-Incorporated Adaptive Stochastic Gradient Descent Algorithm for Latent Factor AnalysisIEEE Transactions on Automation Science and Engineering10.1109/TASE.2023.328481921:3(3742-3756)Online publication date: Jul-2024
https://doi.org/10.1109/TASE.2023.3284819
Li JZhou CYuan Y(2023)An Adaptive PID-Incorporated Non-Negative Latent Factor Analysis Model2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00060(424-428)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00060
Yuan YHe QLuo XShang M(2022)A Multilayered-and-Randomized Latent Factor Model for High-Dimensional and Sparse MatricesIEEE Transactions on Big Data10.1109/TBDATA.2020.29887788:3(784-794)Online publication date: 1-Jun-2022
https://doi.org/10.1109/TBDATA.2020.2988778
Al-Ghossein MAbdessalem TBARRÉ A(2021)A Survey on Stream-Based Recommender SystemsACM Computing Surveys10.1145/345344354:5(1-36)Online publication date: 25-May-2021
https://dl.acm.org/doi/10.1145/3453443
Lee S(2021)Fuzzy clustering with optimization for collaborative filtering-based recommender systemsJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-021-03552-813:9(4189-4206)Online publication date: 3-Nov-2021
https://doi.org/10.1007/s12652-021-03552-8
Afolabi AToivanen P(2020)Harmonization and Categorization of Metrics and Criteria for Evaluation of Recommender Systems in Healthcare From Dual PerspectivesInternational Journal of E-Health and Medical Communications10.4018/IJEHMC.202001010511:1(69-92)Online publication date: 1-Jan-2020
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Sun JNing ZWang CWang X(2020)Collaborative Filtering Algorithm Based on SDAE and Time Mean Model2020 Eighth International Conference on Advanced Cloud and Big Data (CBD)10.1109/CBD51900.2020.00032(131-136)Online publication date: Dec-2020
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Braslavski PBlinov VBolotova VPertsova KShah CBelkin NByström KHuang JScholer F(2018)How to Evaluate Humorous Response Generation, Seriously?Proceedings of the 2018 Conference on Human Information Interaction & Retrieval10.1145/3176349.3176879(225-228)Online publication date: 1-Mar-2018
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Zhang HMin FZhang ZWang S(2018)Efficient collaborative filtering recommendations with multi-channel feature vectorsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-018-0795-8Online publication date: 3-Mar-2018
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