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Stochastic search for global neighbors selection in collaborative filtering

Authors:

Armelle BrunAuthors Info & Claims

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

Pages 232 - 237

https://doi.org/10.1145/2245276.2245322

Published: 26 March 2012 Publication History

Abstract

Neighborhood based collaborative filtering is a popular approach in recommendation systems. In this paper we propose to apply evolutionary computation to reduce the size of the model used for the recommendation. We formulate the problem of constructing the set of neighbors as an optimization problem that we tackle by stochastic local search. The results we present show that our approach produces a set of global neighbors made up of less than 16% of the entire set of users, thus decreases the size of the model by 84%. Furthermore, this reduction leads to a slight increase of the accuracy of a state of the art clustering based approach, without impacting the coverage.

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Cited By

Horváth TCarvalho A(2017)Evolutionary computing in recommender systemsNatural Computing: an international journal10.1007/s11047-016-9540-y16:3(441-462)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s11047-016-9540-y
Zhang JLin YLin MLiu J(2016)An effective collaborative filtering algorithm based on user preference clusteringApplied Intelligence10.1007/s10489-015-0756-945:2(230-240)Online publication date: 15-Feb-2016
https://doi.org/10.1007/s10489-015-0756-9

Index Terms

Stochastic search for global neighbors selection in collaborative filtering
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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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]

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Published: 26 March 2012

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SAC 2012: ACM Symposium on Applied Computing

March 26 - 30, 2012

Trento, Italy

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

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

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Cited By

Horváth TCarvalho A(2017)Evolutionary computing in recommender systemsNatural Computing: an international journal10.1007/s11047-016-9540-y16:3(441-462)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s11047-016-9540-y
Zhang JLin YLin MLiu J(2016)An effective collaborative filtering algorithm based on user preference clusteringApplied Intelligence10.1007/s10489-015-0756-945:2(230-240)Online publication date: 15-Feb-2016
https://doi.org/10.1007/s10489-015-0756-9

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