research-article

Understanding Similarity Metrics in Neighbour-based Recommender Systems

Authors:

Alejandro Bellogín,

Arjen P. de VriesAuthors Info & Claims

ICTIR '13: Proceedings of the 2013 Conference on the Theory of Information Retrieval

Pages 48 - 55

https://doi.org/10.1145/2499178.2499186

Published: 29 September 2013 Publication History

Abstract

Neighbour-based collaborative filtering is a recommendation technique that provides meaningful and, usually, accurate recommendations. The method's success depends however critically upon the similarity metric used to find the most similar users (neighbours), the basis of the predictions made. In this paper, we explore twelve features that aim to explain why some user similarity metrics perform better than others. Specifically, we define two sets of features, a first one based on statistics computed over the distance distribution in the neighbourhood, and, a second one based on the nearest neighbour graph. Our experiments with a public dataset show that some of these features are able to correlate with the performance up to a 90%.

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Cited By

Poudel SBikdash M(2023)Closed-Form Models of Accuracy Loss due to Subsampling in SVD Collaborative FilteringBig Data Mining and Analytics10.26599/BDMA.2022.90200246:1(72-84)Online publication date: Mar-2023
https://doi.org/10.26599/BDMA.2022.9020024
Chaitanya VMohan MThilagam P(2022)A Clustering-based model for the Generation of Diversified Recommendations2022 IEEE Silchar Subsection Conference (SILCON)10.1109/SILCON55242.2022.10028791(1-6)Online publication date: 4-Nov-2022
https://doi.org/10.1109/SILCON55242.2022.10028791
Dong ZLeng CZheng H(2020)Employment Service System Based on Hybrid Recommendation AlgorithmBig Data Analytics for Cyber-Physical System in Smart City10.1007/978-981-33-4572-0_54(368-375)Online publication date: 18-Dec-2020
https://doi.org/10.1007/978-981-33-4572-0_54
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Index Terms

Understanding Similarity Metrics in Neighbour-based Recommender Systems
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Other conferences

ICTIR '13: Proceedings of the 2013 Conference on the Theory of Information Retrieval

September 2013

148 pages

ISBN:9781450321075

DOI:10.1145/2499178

Editors:
Oren Kurland
Technion - Israel Institute of Technology
,
Donald Metzler
Google, USA
,
Christina Lioma
University of Copenhagen, Denmark
,
Birger Larsen
University of Copenhagen, Denmark
,
Peter Ingwersen
University of Copenhagen, Denmark

Copyright © 2013 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 the author(s) 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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Findwise: Findwise AB
Google Inc.
Spinque: Spinque
Univ. of Copenhagen: University of Copenhagen
LARM: LARM Audio Research Archive
Royal School of Library and Information Science: Royal School of Library and Information Science
Yahoo! Labs

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SIGIR: ACM Special Interest Group on Information Retrieval
British Computer Society: BCS

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 September 2013

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ICTIR '13

Sponsor:

Findwise
Spinque
Univ. of Copenhagen
LARM
Royal School of Library and Information Science

ICTIR '13: International Conference on the Theory of Information Retrieval

September 29 - October 2, 2013

Copenhagen, Denmark

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ICTIR '13 Paper Acceptance Rate 11 of 51 submissions, 22%;

Overall Acceptance Rate 235 of 527 submissions, 45%

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Cited By

Poudel SBikdash M(2023)Closed-Form Models of Accuracy Loss due to Subsampling in SVD Collaborative FilteringBig Data Mining and Analytics10.26599/BDMA.2022.90200246:1(72-84)Online publication date: Mar-2023
https://doi.org/10.26599/BDMA.2022.9020024
Chaitanya VMohan MThilagam P(2022)A Clustering-based model for the Generation of Diversified Recommendations2022 IEEE Silchar Subsection Conference (SILCON)10.1109/SILCON55242.2022.10028791(1-6)Online publication date: 4-Nov-2022
https://doi.org/10.1109/SILCON55242.2022.10028791
Dong ZLeng CZheng H(2020)Employment Service System Based on Hybrid Recommendation AlgorithmBig Data Analytics for Cyber-Physical System in Smart City10.1007/978-981-33-4572-0_54(368-375)Online publication date: 18-Dec-2020
https://doi.org/10.1007/978-981-33-4572-0_54
Bellogín ASánchez PTramullas JTrillo-Lado RNogueras-Iso J(2018)Applying Subsequence Matching to Collaborative FilteringProceedings of the 5th Spanish Conference on Information Retrieval10.1145/3230599.3230605(1-2)Online publication date: 26-Jun-2018
https://dl.acm.org/doi/10.1145/3230599.3230605
Claveau V(2018)Indiscriminateness in Representation Spaces of Terms and DocumentsAdvances in Information Retrieval10.1007/978-3-319-76941-7_19(251-262)Online publication date: 1-Mar-2018
https://doi.org/10.1007/978-3-319-76941-7_19
Bellogín ASaid A(2018)Recommender Systems EvaluationEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_110162(2095-2112)Online publication date: 12-Jun-2018
https://doi.org/10.1007/978-1-4939-7131-2_110162
Bellogín ASaid A(2017)Recommender Systems EvaluationEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4614-7163-9_110162-1(1-18)Online publication date: 6-Jul-2017
https://doi.org/10.1007/978-1-4614-7163-9_110162-1
Al-Shamri M(2016)Effect of Collaborative Recommender System ParametersAdvances in Artificial Intelligence10.1155/2016/93863682016(1)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1155/2016/9386368
Sattari MToroslu IKaragoz PSymeonidis PManolopoulos Y(2015)Extended feature combination model for recommendations in location-based mobile servicesKnowledge and Information Systems10.1007/s10115-014-0776-544:3(629-661)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1007/s10115-014-0776-5
Bedi PGautam ARicha Sharma C(2014)Using novelty score of unseen items to handle popularity bias in recommender systems2014 International Conference on Contemporary Computing and Informatics (IC3I)10.1109/IC3I.2014.7019608(934-939)Online publication date: Nov-2014
https://doi.org/10.1109/IC3I.2014.7019608

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