research-article

A Probabilistic Reformulation of Memory-Based Collaborative Filtering: Implications on Popularity Biases

Authors:

Rocío Cañamares,

Pablo CastellsAuthors Info & Claims

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 215 - 224

https://doi.org/10.1145/3077136.3080836

Published: 07 August 2017 Publication History

Abstract

We develop a probabilistic formulation giving rise to a formal version of heuristic k nearest-neighbor (kNN) collaborative filtering. Different independence assumptions in our scheme lead to user-based, item-based, normalized and non-normalized variants that match in structure the traditional formulations, while showing equivalent empirical effectiveness. The probabilistic formulation provides a principled explanation why kNN is an effective recommendation strategy, and identifies a key condition for this to be the case. Moreover, a natural explanation arises for the bias of kNN towards recommending popular items. Thereupon the kNN variants are shown to fall into two groups with similar trends in behavior, corresponding to two different notions of item popularity. We show experiments where the comparative performance of the two groups of algorithms changes substantially, which suggests that the performance measurements and comparison may heavily depend on statistical properties of the input data sample.

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Cited By

Bauer CZangerle ESaid A(2024)Exploring the Landscape of Recommender Systems Evaluation: Practices and PerspectivesACM Transactions on Recommender Systems10.1145/36291702:1(1-31)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3629170
Ning WCheng RYan XKao BHuo NHaldar NTang BChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Debiasing Recommendation with Personal PopularityProceedings of the ACM Web Conference 202410.1145/3589334.3645421(3400-3409)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645421
Diboune ASlimani HNacer HBeghdad Bey K(2024)A comprehensive survey on community detection methods and applications in complex information networksSocial Network Analysis and Mining10.1007/s13278-024-01246-514:1Online publication date: 18-Apr-2024
https://doi.org/10.1007/s13278-024-01246-5
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Index Terms

A Probabilistic Reformulation of Memory-Based Collaborative Filtering: Implications on Popularity Biases
1. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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cover image ACM Conferences

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2017

1476 pages

ISBN:9781450350228

DOI:10.1145/3077136

General Chairs:
Noriko Kando
National Institute of Informatics
,
Tetsuya Sakai
Waseda University
,
Hideo Joho
University of Tsukuba
,
Program Chairs:
Hang Li
Huawei Noah's Ark Lab
,
Arjen P. de Vries
Radboud University
,
Ryen W. White
Microsoft Cortana

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Published: 07 August 2017

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SIGIR '17: The 40th International ACM SIGIR conference on research and development in Information Retrieval

August 7 - 11, 2017

Tokyo, Shinjuku, Japan

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SIGIR '17 Paper Acceptance Rate 78 of 362 submissions, 22%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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https://dl.acm.org/doi/10.1145/3629170
Ning WCheng RYan XKao BHuo NHaldar NTang BChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Debiasing Recommendation with Personal PopularityProceedings of the ACM Web Conference 202410.1145/3589334.3645421(3400-3409)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645421
Diboune ASlimani HNacer HBeghdad Bey K(2024)A comprehensive survey on community detection methods and applications in complex information networksSocial Network Analysis and Mining10.1007/s13278-024-01246-514:1Online publication date: 18-Apr-2024
https://doi.org/10.1007/s13278-024-01246-5
Abinaya SIndira KKarthiga SRajasenbagam T(2023)Time Cluster Personalized Ranking Recommender System in Multi-CloudMathematics10.3390/math1106130011:6(1300)Online publication date: 8-Mar-2023
https://doi.org/10.3390/math11061300
Ekstrand MCarterette BDiaz F(2023)Distributionally-Informed Recommender System EvaluationACM Transactions on Recommender Systems10.1145/36134552:1(1-27)Online publication date: 5-Aug-2023
https://dl.acm.org/doi/10.1145/3613455
Sheth PGuo RCheng LLiu HCandan K(2023)Causal Disentanglement for Implicit Recommendations with Network InformationACM Transactions on Knowledge Discovery from Data10.1145/358243517:7(1-18)Online publication date: 6-Apr-2023
https://dl.acm.org/doi/10.1145/3582435
Sánchez PBellogín ABoratto L(2023)Bias characterization, assessment, and mitigation in location-based recommender systemsData Mining and Knowledge Discovery10.1007/s10618-022-00913-537:5(1885-1929)Online publication date: 14-Feb-2023
https://doi.org/10.1007/s10618-022-00913-5
Zhang CHoutti MSmith CKong RTerveen L(2022)Working for the Invisible Machines or Pumping Information into an Empty Void? An Exploration of Wikidata Contributors' MotivationsProceedings of the ACM on Human-Computer Interaction10.1145/35129826:CSCW1(1-21)Online publication date: 7-Apr-2022
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Zhao YSu TLiu YZheng WWu XKavuluru RHalfond WYu T(2022)ReCDroid+: Automated End-to-End Crash Reproduction from Bug Reports for Android AppsACM Transactions on Software Engineering and Methodology10.1145/348824431:3(1-33)Online publication date: 7-Mar-2022
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Wang JYang YWang SHu JWang Q(2022)Context- and Fairness-Aware In-Process Crowdworker RecommendationACM Transactions on Software Engineering and Methodology10.1145/348757131:3(1-31)Online publication date: 7-Mar-2022
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