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The efficient imputation method for neighborhood-based collaborative filtering

Authors:

Wanlei ZhouAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 684 - 693

https://doi.org/10.1145/2396761.2396849

Published: 29 October 2012 Publication History

Abstract

As each user tends to rate a small proportion of available items, the resulted Data Sparsity issue brings significant challenges to the research of recommender systems. This issue becomes even more severe for neighborhood-based collaborative filtering methods, as there are even lower numbers of ratings available in the neighborhood of the query item. In this paper, we aim to address the Data Sparsity issue in the context of the neighborhood-based collaborative filtering. Given the (user, item) query, a set of key ratings are identified, and an auto-adaptive imputation method is proposed to fill the missing values in the set of key ratings. The proposed method can be used with any similarity metrics, such as the Pearson Correlation Coefficient and Cosine-based similarity, and it is theoretically guaranteed to outperform the neighborhood-based collaborative filtering approaches. Results from experiments prove that the proposed method could significantly improve the accuracy of recommendations for neighborhood-based Collaborative Filtering algorithms.

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Cited By

Li JRen YSanderson MDeng K(2024)Explaining Recommendation Fairness from a User/Item PerspectiveACM Transactions on Information Systems10.1145/369887743:1(1-30)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3698877
Shaikh SKagita VKumar VPujari A(2024)Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorizationExpert Systems with Applications10.1016/j.eswa.2023.121967238(121967)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121967
Wang CZhu HZhu CQin CChen EXiong H(2023)SetRank: A Setwise Bayesian Approach for Collaborative Ranking in Recommender SystemACM Transactions on Information Systems10.1145/362619442:2(1-32)Online publication date: 3-Oct-2023
https://dl.acm.org/doi/10.1145/3626194
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Index Terms

The efficient imputation method for neighborhood-based collaborative filtering
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications

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cover image ACM Conferences

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

Copyright © 2012 ACM.

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Published: 29 October 2012

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October 29 - November 2, 2012

Hawaii, Maui, USA

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Cited By

Li JRen YSanderson MDeng K(2024)Explaining Recommendation Fairness from a User/Item PerspectiveACM Transactions on Information Systems10.1145/369887743:1(1-30)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3698877
Shaikh SKagita VKumar VPujari A(2024)Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorizationExpert Systems with Applications10.1016/j.eswa.2023.121967238(121967)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121967
Wang CZhu HZhu CQin CChen EXiong H(2023)SetRank: A Setwise Bayesian Approach for Collaborative Ranking in Recommender SystemACM Transactions on Information Systems10.1145/362619442:2(1-32)Online publication date: 3-Oct-2023
https://dl.acm.org/doi/10.1145/3626194
Lu XWu JHuang JLuo FYuan J(2023)Co-Training-Teaching: A Robust Semi-Supervised Framework for Review-Aware Rating RegressionACM Transactions on Knowledge Discovery from Data10.1145/362539118:2(1-16)Online publication date: 26-Sep-2023
https://dl.acm.org/doi/10.1145/3625391
Chaimalas IWalker DGruppi EClark BToni L(2023)Bootstrapped Personalized Popularity for Cold Start Recommender SystemsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608820(715-722)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608820
Hwang SChae DAl Hasan MXiong L(2022)An Uncertainty-Aware Imputation Framework for Alleviating the Sparsity Problem in Collaborative FilteringProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557236(802-811)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557236
Li PSu XWu WZhu XXing H(2022)Research on Different Weights of Single-Valued Neutrosophic Sets in Recommendation System2022 5th International Conference on Data Science and Information Technology (DSIT)10.1109/DSIT55514.2022.9943972(01-06)Online publication date: 22-Jul-2022
https://doi.org/10.1109/DSIT55514.2022.9943972
Wang JRen YLi JDeng K(2021)The Footprint of Factorization Models and Their Applications in Collaborative FilteringACM Transactions on Information Systems10.1145/349047540:4(1-32)Online publication date: 29-Nov-2021
https://dl.acm.org/doi/10.1145/3490475
Bae HKim HShin WKim S(2021)“How to get consensus with neighbors?”Knowledge-Based Systems10.1016/j.knosys.2021.107549234:COnline publication date: 25-Dec-2021
https://dl.acm.org/doi/10.1016/j.knosys.2021.107549
Wu JSang XCui W(2021)Semi-supervised collaborative filtering ensembleWorld Wide Web10.1007/s11280-021-00866-7Online publication date: 12-Mar-2021
https://doi.org/10.1007/s11280-021-00866-7
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