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Swarming to rank for recommender systems

Published: 09 September 2012 Publication History

Abstract

Recommender systems make product suggestions that are tailored to the user's individual needs and represent powerful means to combat information overload. In this paper, we focus on the item prediction task of Recommender Systems and present SwarmRankCF, a method to automatically optimize the performance quality of recommender systems using a Swarm Intelligence perspective. Our approach, which is well-founded in a Particle Swarm Optimization framework, learns a ranking function by optimizing the combination of unique characteristics (i.e., features) of users, items and their interactions. In particular, we build feature vectors from a factorization of the user-item interaction matrix, and directly optimize Mean Average Precision metric in order to learn a linear ranking model for personalized recommendations. Our experimental evaluation, on a real world online radio dataset, indicates that our approach is able to find ranking functions that significantly improve the performance of the system for the Top-N recommendation task.

References

[1]
J. S. Breese, D. Heckerman, and C. M. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Conference on Uncertainty in Artificial Intelligence, 1998.
[2]
C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to Rank Using Gradient Descent. In Proceedings of the ICML, 2005.
[3]
O. Celma. Music Recommendation and Discovery in the Long Tail. Springer, 2010.
[4]
P. Cremonesi, Y. Koren, and R. Turrin. Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the ACM RecSys conference, 2010.
[5]
E. Diaz-Aviles, W. Nejdl, and L. Schmidt-Thieme. Swarming to rank for information retrieval. In Proceedings of the ACM GECCO conference, 2009.
[6]
Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res., 4:933--969, 2003.
[7]
R. Herbrich, T. Graepel, and K. Obermayer. Large Margin Rank Boundaries for Ordinal Regression. In Advances in Large Margin Classifiers, 2000.
[8]
T. Joachims. Optimizing search engines using clickthrough data. In Proceedings of the ACM KDD conference, 2002.
[9]
Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42:30--37, August 2009.
[10]
T.-Y. Liu. Learning to Rank for Information Retrieval. Springer, 2011.
[11]
R. Poli, J. Kennedy, and T. Blackwell. Particle swarm optimization. Swarm Intelligence, 1(1):33--57, 2007.
[12]
M. Wetter. Generic Optimization Program -- GenOpt. User Manual,User Manual Version 3.1.0. Lawrence Berkeley National Laboratory., 2011.

Cited By

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  • (2020)Ant Collaborative Filtering Addressing Sparsity and Temporal EffectsIEEE Access10.1109/ACCESS.2020.29739318(32783-32791)Online publication date: 2020
  • (2019)Discovery of user-item subgroups via genetic algorithm for effective prediction of ratings in collaborative filteringApplied Intelligence10.1007/s10489-019-01495-449:11(3990-4006)Online publication date: 1-Nov-2019
  • (2018)Multiobjective recommendation optimization via utilizing distributed parallel algorithmFuture Generation Computer Systems10.1016/j.future.2017.09.00586:C(1259-1268)Online publication date: 1-Sep-2018
  • Show More Cited By

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cover image ACM Conferences
RecSys '12: Proceedings of the sixth ACM conference on Recommender systems
September 2012
376 pages
ISBN:9781450312707
DOI:10.1145/2365952
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 September 2012

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Author Tags

  1. PSO
  2. collaborative filtering
  3. matrix factorization
  4. recommender systems
  5. swarm intelligence

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  • Short-paper

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RecSys '12
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RecSys '12: Sixth ACM Conference on Recommender Systems
September 9 - 13, 2012
Dublin, Ireland

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RecSys '12 Paper Acceptance Rate 24 of 119 submissions, 20%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

View all
  • (2020)Ant Collaborative Filtering Addressing Sparsity and Temporal EffectsIEEE Access10.1109/ACCESS.2020.29739318(32783-32791)Online publication date: 2020
  • (2019)Discovery of user-item subgroups via genetic algorithm for effective prediction of ratings in collaborative filteringApplied Intelligence10.1007/s10489-019-01495-449:11(3990-4006)Online publication date: 1-Nov-2019
  • (2018)Multiobjective recommendation optimization via utilizing distributed parallel algorithmFuture Generation Computer Systems10.1016/j.future.2017.09.00586:C(1259-1268)Online publication date: 1-Sep-2018
  • (2016)Collaborative Filtering, Matrix Factorization and Population Based Search: The Nexus UnveiledNeural Information Processing10.1007/978-3-319-46675-0_39(352-361)Online publication date: 29-Sep-2016
  • (2015)Music Recommender SystemsRecommender Systems Handbook10.1007/978-1-4899-7637-6_13(453-492)Online publication date: 2015
  • (2014)Predicting User Engagement in Twitter with Collaborative RankingProceedings of the 2014 Recommender Systems Challenge10.1145/2668067.2668072(41-46)Online publication date: 10-Oct-2014
  • (2013)Mining large streams of user data for personalized recommendationsACM SIGKDD Explorations Newsletter10.1145/2481244.248125014:2(37-48)Online publication date: 30-Apr-2013
  • (2013)Recommendation with Differential Context WeightingUser Modeling, Adaptation, and Personalization10.1007/978-3-642-38844-6_13(152-164)Online publication date: 2013

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