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research-article

Sequential User-based Recurrent Neural Network Recommendations

Authors:

Benedikt Loepp,

Jürgen ZieglerAuthors Info & Claims

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

Pages 152 - 160

https://doi.org/10.1145/3109859.3109877

Published: 27 August 2017 Publication History

Abstract

Recurrent Neural Networks are powerful tools for modeling sequences. They are flexibly extensible and can incorporate various kinds of information including temporal order. These properties make them well suited for generating sequential recommendations. In this paper, we extend Recurrent Neural Networks by considering unique characteristics of the Recommender Systems domain. One of these characteristics is the explicit notion of the user recommendations are specifically generated for. We show how individual users can be represented in addition to sequences of consumed items in a new type of Gated Recurrent Unit to effectively produce personalized next item recommendations. Offline experiments on two real-world datasets indicate that our extensions clearly improve objective performance when compared to state-of-the-art recommender algorithms and to a conventional Recurrent Neural Network.

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Brahim DFahd KEl Habib NAbdelhak B(2025)Neural Microblogging Followee Recommender System Based on Pre-trained Transformer, and Topic ModelsBig Data and Internet of Things10.1007/978-3-031-74491-4_3(32-47)Online publication date: 3-Jan-2025
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He HYang XHuang FYi FLiang S(2024)GAT4Rec: Sequential Recommendation with a Gated Recurrent Unit and TransformersMathematics10.3390/math1214218912:14(2189)Online publication date: 12-Jul-2024
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El-Shaer EMcKee GHamdy A(2024)The LSTM-EMPG Model for Next Basket Recommendation in E-commerceInternational Journal of Information and Communication Sciences10.11648/j.ijics.20240901.129:1(9-23)Online publication date: 15-Jul-2024
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Index Terms

Sequential User-based Recurrent Neural Network Recommendations
1. Computing methodologies
  1. Artificial intelligence
2. Information systems
  1. Information retrieval
    1. 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 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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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

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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

Brahim DFahd KEl Habib NAbdelhak B(2025)Neural Microblogging Followee Recommender System Based on Pre-trained Transformer, and Topic ModelsBig Data and Internet of Things10.1007/978-3-031-74491-4_3(32-47)Online publication date: 3-Jan-2025
https://doi.org/10.1007/978-3-031-74491-4_3
He HYang XHuang FYi FLiang S(2024)GAT4Rec: Sequential Recommendation with a Gated Recurrent Unit and TransformersMathematics10.3390/math1214218912:14(2189)Online publication date: 12-Jul-2024
https://doi.org/10.3390/math12142189
El-Shaer EMcKee GHamdy A(2024)The LSTM-EMPG Model for Next Basket Recommendation in E-commerceInternational Journal of Information and Communication Sciences10.11648/j.ijics.20240901.129:1(9-23)Online publication date: 15-Jul-2024
https://doi.org/10.11648/j.ijics.20240901.12
Zhang LZhou XZeng ZShen Z(2024)Multimodal Pre-training for Sequential Recommendation via Contrastive LearningACM Transactions on Recommender Systems10.1145/36820753:1(1-23)Online publication date: 29-Jul-2024
https://dl.acm.org/doi/10.1145/3682075
Sahebi SYao MZhao SFeyzi Behnagh R(2024)MoMENt: Marked Point Processes with Memory-Enhanced Neural Networks for User Activity ModelingACM Transactions on Knowledge Discovery from Data10.1145/364950418:6(1-32)Online publication date: 29-Feb-2024
https://dl.acm.org/doi/10.1145/3649504
Lin JPeng SZhang ZZhao P(2024)TLRec: A Transfer Learning Framework to Enhance Large Language Models for Sequential Recommendation TasksProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691710(1119-1124)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691710
Liu YLiu MLiu X(2024)Pay Attention to Attention for Sequential RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688164(890-895)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688164
Qu ZXie RXiao CKang ZSun X(2024)The Elephant in the Room: Rethinking the Usage of Pre-trained Language Model in Sequential RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688107(53-62)Online publication date: 8-Oct-2024
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Du YWang ZSun ZMa YLiu HZhang JBaeza-Yates RBonchi F(2024)Disentangled Multi-interest Representation Learning for Sequential RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671800(677-688)Online publication date: 25-Aug-2024
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Bai ZWu NCai FZhu XXiong YSerra ESpezzano F(2024)Aligning Large Language Model with Direct Multi-Preference Optimization for RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679611(76-86)Online publication date: 21-Oct-2024
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