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Local Item-Item Models For Top-N Recommendation

Authors:

Evangelia Christakopoulou,

George KarypisAuthors Info & Claims

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

Pages 67 - 74

https://doi.org/10.1145/2959100.2959185

Published: 07 September 2016 Publication History

Abstract

Item-based approaches based on SLIM (Sparse LInear Methods) have demonstrated very good performance for top-N recommendation; however they only estimate a single model for all the users. This work is based on the intuition that not all users behave in the same way -- instead there exist subsets of like-minded users. By using different item-item models for these user subsets, we can capture differences in their preferences and this can lead to improved performance for top-N recommendations. In this work, we extend SLIM by combining global and local SLIM models. We present a method that computes the prediction scores as a user-specific combination of the predictions derived by a global and local item-item models. We present an approach in which the global model, the local models, their user-specific combination, and the assignment of users to the local models are jointly optimized to improve the top-N recommendation performance. Our experiments show that the proposed method improves upon the standard SLIM model and outperforms competing top-N recommendation approaches.

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Cited By

Wei TChow TMa J(2024)FPSR+: Toward Robust, Efficient, and Scalable Collaborative Filtering With Partition-Aware Item Similarity ModelingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.341808036:12(8283-8296)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3418080
Kong LDing HHu G(2024)GCNSLIM: Graph convolutional network with sparse linear methods for e-government service recommendationKnowledge-Based Systems10.1016/j.knosys.2024.111593292(111593)Online publication date: May-2024
https://doi.org/10.1016/j.knosys.2024.111593
Zhou WHaq AQiu LAkbar J(2024)Multi-view social recommendation via matrix factorization with sub-linear convergence rateExpert Systems with Applications10.1016/j.eswa.2023.121687237(121687)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121687
Show More Cited By

Index Terms

Local Item-Item Models For Top-N Recommendation
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Collaborative filtering
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

September 2016

490 pages

ISBN:9781450340359

DOI:10.1145/2959100

General Chairs:
Shilad Sen
Macalester College, USA
,
Werner Geyer
IBM Research Cambridge, USA
,
Program Chairs:
Jill Freyne
CSIRO, Australia
,
Pablo Castells
Universidad Autónoma de Madrid, Spain

Copyright © 2016 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: 07 September 2016

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National Science Foundation
Intel Software and Services Group
Army Research Office
Digital Technology Center at the University of Minnesota

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RecSys '16

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SIGCHI

RecSys '16: Tenth ACM Conference on Recommender Systems

September 15 - 19, 2016

Massachusetts, Boston, USA

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RecSys '16 Paper Acceptance Rate 29 of 159 submissions, 18%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Wei TChow TMa J(2024)FPSR+: Toward Robust, Efficient, and Scalable Collaborative Filtering With Partition-Aware Item Similarity ModelingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.341808036:12(8283-8296)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3418080
Kong LDing HHu G(2024)GCNSLIM: Graph convolutional network with sparse linear methods for e-government service recommendationKnowledge-Based Systems10.1016/j.knosys.2024.111593292(111593)Online publication date: May-2024
https://doi.org/10.1016/j.knosys.2024.111593
Zhou WHaq AQiu LAkbar J(2024)Multi-view social recommendation via matrix factorization with sub-linear convergence rateExpert Systems with Applications10.1016/j.eswa.2023.121687237(121687)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121687
Paul AWan YWu ZChen BGong S(2024)Deep recommendation with iteration directional adversarial trainingComputing10.1007/s00607-024-01326-6106:10(3151-3174)Online publication date: 17-Jul-2024
https://doi.org/10.1007/s00607-024-01326-6
Sun SCai TYan FJu S(2024)Two-Stage Enhancement for Recommendation Systems Based on Contrastive LearningWeb Information Systems and Applications10.1007/978-981-97-7707-5_13(150-162)Online publication date: 11-Sep-2024
https://doi.org/10.1007/978-981-97-7707-5_13
Hu Y(2024)Matrix Factorization Model in Collaborative Filtering Algorithms Based on Feedback DatasetsProceedings of the 7th International Conference on Economic Management and Green Development10.1007/978-981-97-0523-8_128(1405-1412)Online publication date: 27-Feb-2024
https://doi.org/10.1007/978-981-97-0523-8_128
Tomlinson KWan MLu CHecht BTeevan JYang L(2023)Targeted Training for Multi-organization RecommendationACM Transactions on Recommender Systems10.1145/36035081:3(1-18)Online publication date: 14-Jul-2023
https://dl.acm.org/doi/10.1145/3603508
Jiang JZhang PLuo YLi CKim JZhang KWang SXie XKim SFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614773(976-986)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614773
Fan RPu YChen JZhu ZLian DChen E(2023)AutoS2AE: Automate to Regularize Sparse Shallow Autoencoders for RecommendationProceedings of the ACM Web Conference 202310.1145/3543507.3583349(1032-1042)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583349
Wei TMa JChow T(2023)Fine-tuning Partition-aware Item Similarities for Efficient and Scalable RecommendationProceedings of the ACM Web Conference 202310.1145/3543507.3583240(823-832)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583240
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