short-paper

On Parallelizing SGD for Pairwise Learning to Rank in Collaborative Filtering Recommender Systems

Authors:

Tevfik Aytekin,

Fikret GurgenAuthors Info & Claims

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

Pages 37 - 41

https://doi.org/10.1145/3109859.3109906

Published: 27 August 2017 Publication History

Abstract

Learning to rank with pairwise loss functions has been found useful in collaborative filtering recommender systems. At web scale, the optimization is often based on matrix factorization with stochastic gradient descent (SGD) which has a sequential nature. We investigate two different shared memory lock-free parallel SGD schemes based on block partitioning and no partitioning for use with pairwise loss functions. To speed up convergence to a solution, we extrapolate simple practical algorithms from their application to pointwise learning to rank. Experimental results show that the proposed algorithms are quite useful regarding their ranking ability and speedup patterns in comparison to their sequential counterpart.

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Cited By

Gulcan SOzdal MAykanat C(2023)Load balanced locality-aware parallel SGD on multicore architectures for latent factor based collaborative filteringFuture Generation Computer Systems10.1016/j.future.2023.04.007146(207-221)Online publication date: Sep-2023
https://doi.org/10.1016/j.future.2023.04.007
Ravanmehr RMohamadrezaei RRavanmehr RMohamadrezaei R(2023)Learning to Rank in Session-Based Recommender SystemsSession-Based Recommender Systems Using Deep Learning10.1007/978-3-031-42559-2_6(245-292)Online publication date: 21-Dec-2023
https://doi.org/10.1007/978-3-031-42559-2_6
Awati CShirgave S(2022)The State of the Art Techniques in Recommendation SystemsApplied Computational Technologies10.1007/978-981-19-2719-5_68(730-741)Online publication date: 15-May-2022
https://doi.org/10.1007/978-981-19-2719-5_68
Show More Cited By

Index Terms

On Parallelizing SGD for Pairwise Learning to Rank in Collaborative Filtering Recommender Systems
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Learning to rank
    2. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

August 2017

466 pages

ISBN:9781450346528

DOI:10.1145/3109859

General Chairs:
Paolo Cremonesi
Politecnico di Milano, Italy
,
Francesco Ricci
Free University Bozen-Bolzano, Italy
,
Program Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Alexander Tuzhilin
New York University, USA

Copyright © 2017 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 August 2017

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RecSys '17

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SIGCHI

RecSys '17: Eleventh ACM Conference on Recommender Systems

August 27 - 31, 2017

Como, Italy

Acceptance Rates

RecSys '17 Paper Acceptance Rate 26 of 125 submissions, 21%;

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

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Cited By

Gulcan SOzdal MAykanat C(2023)Load balanced locality-aware parallel SGD on multicore architectures for latent factor based collaborative filteringFuture Generation Computer Systems10.1016/j.future.2023.04.007146(207-221)Online publication date: Sep-2023
https://doi.org/10.1016/j.future.2023.04.007
Ravanmehr RMohamadrezaei RRavanmehr RMohamadrezaei R(2023)Learning to Rank in Session-Based Recommender SystemsSession-Based Recommender Systems Using Deep Learning10.1007/978-3-031-42559-2_6(245-292)Online publication date: 21-Dec-2023
https://doi.org/10.1007/978-3-031-42559-2_6
Awati CShirgave S(2022)The State of the Art Techniques in Recommendation SystemsApplied Computational Technologies10.1007/978-981-19-2719-5_68(730-741)Online publication date: 15-May-2022
https://doi.org/10.1007/978-981-19-2719-5_68
D’Addio RMarinho RManzato M(2019)Combining different metadata views for better recommendation accuracyInformation Systems10.1016/j.is.2019.01.00883:C(1-12)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1016/j.is.2019.01.008
Yağcı AAytekin TGürgen F(2019)Parallel pairwise learning to rank for collaborative filteringConcurrency and Computation: Practice and Experience10.1002/cpe.514131:15Online publication date: 22-Jan-2019
https://doi.org/10.1002/cpe.5141
Zhang JWang XFeng YSun MLi PCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)FastInputProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3272006(2057-2065)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3272006
Jameel MSchilling NSchmidt-Thieme LCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)Towards Distributed Pairwise Ranking using Implicit FeedbackThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210113(973-976)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210113
Wang SHuang JLee WLee K(2018)Scaling Up Matrix Factorization with Cloud Computing for Collaborative Recommendation2018 International Conference on System Science and Engineering (ICSSE)10.1109/ICSSE.2018.8520095(1-6)Online publication date: Jun-2018
https://doi.org/10.1109/ICSSE.2018.8520095
Gulcan SOzdal MAykanat C(undefined)Load Balanced Locality-Aware Parallel Sgd on Multicore Architectures for Latent Factor Based Collaborative FilteringSSRN Electronic Journal10.2139/ssrn.4184264
https://doi.org/10.2139/ssrn.4184264

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