research-article

Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations

Authors:

Balázs Hidasi,

Massimo Quadrana,

Alexandros Karatzoglou,

Domonkos TikkAuthors Info & Claims

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

Pages 241 - 248

https://doi.org/10.1145/2959100.2959167

Published: 07 September 2016 Publication History

Abstract

Real-life recommender systems often face the daunting task of providing recommendations based only on the clicks of a user session. Methods that rely on user profiles -- such as matrix factorization -- perform very poorly in this setting, thus item-to-item recommendations are used most of the time. However the items typically have rich feature representations such as pictures and text descriptions that can be used to model the sessions. Here we investigate how these features can be exploited in Recurrent Neural Network based session models using deep learning. We show that obvious approaches do not leverage these data sources. We thus introduce a number of parallel RNN (p-RNN) architectures to model sessions based on the clicks and the features (images and text) of the clicked items. We also propose alternative training strategies for p-RNNs that suit them better than standard training. We show that p-RNN architectures with proper training have significant performance improvements over feature-less session models while all session-based models outperform the item-to-item type baseline.

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Cited By

Li DTian THuang ZLin XChen DMadden A(2025)Improved session recommendation using contrastive learning based tail adjusted repeat aware graph neural networkComputer Science and Information Systems10.2298/CSIS231101013L22:1(345-368)Online publication date: 2025
https://doi.org/10.2298/CSIS231101013L
El Alaoui DRiffi JSabri AAghoutane BYahyaouy ATairi H(2025)A Novel session-based recommendation system using capsule graph neural networkNeural Networks10.1016/j.neunet.2025.107176185(107176)Online publication date: May-2025
https://doi.org/10.1016/j.neunet.2025.107176
Khan ZKhan ZIltaf N(2025)ConvSeq-MFNeurocomputing10.1016/j.neucom.2024.128932618:COnline publication date: 14-Feb-2025
https://dl.acm.org/doi/10.1016/j.neucom.2024.128932
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Index Terms

Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. World Wide Web
    1. Web searching and information discovery
      1. Collaborative filtering

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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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EU FP7

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RecSys '16

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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

Li DTian THuang ZLin XChen DMadden A(2025)Improved session recommendation using contrastive learning based tail adjusted repeat aware graph neural networkComputer Science and Information Systems10.2298/CSIS231101013L22:1(345-368)Online publication date: 2025
https://doi.org/10.2298/CSIS231101013L
El Alaoui DRiffi JSabri AAghoutane BYahyaouy ATairi H(2025)A Novel session-based recommendation system using capsule graph neural networkNeural Networks10.1016/j.neunet.2025.107176185(107176)Online publication date: May-2025
https://doi.org/10.1016/j.neunet.2025.107176
Khan ZKhan ZIltaf N(2025)ConvSeq-MFNeurocomputing10.1016/j.neucom.2024.128932618:COnline publication date: 14-Feb-2025
https://dl.acm.org/doi/10.1016/j.neucom.2024.128932
Wang YZhu JDuan LLi ASun JWang CLi Z(2025)Disentangled Sparse Graph Attention Networks with Multi-Intent Fusion for Session-based RecommendationKnowledge-Based Systems10.1016/j.knosys.2025.113082311(113082)Online publication date: Feb-2025
https://doi.org/10.1016/j.knosys.2025.113082
Cheng MZhang HLiu QYuan FLi ZHuang ZChen EZhou JLi L(2025)Empowering Sequential Recommendation from Collaborative Signals and Semantic RelatednessDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_13(196-211)Online publication date: 12-Jan-2025
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Liu YQi JYu Y(2025)A Review on Deep Learning for Sequential Recommender Systems: Key Technologies and DirectionsBig Data10.1007/978-981-96-1024-2_22(305-318)Online publication date: 24-Jan-2025
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Arockia Venice JArivazhagan DSuman NShanthi HSwadhi R(2024)Recommendation Systems and Content PersonalizationAI for Large Scale Communication Networks10.4018/979-8-3693-6552-6.ch015(323-348)Online publication date: 25-Oct-2024
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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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Lin MWu PHsueh S(2024)Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction IntegrationFuture Internet10.3390/fi1602005116:2(51)Online publication date: 1-Feb-2024
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Mao YLin SCheng Y(2024)A Job Recommendation Model Based on a Two-Layer Attention MechanismElectronics10.3390/electronics1303048513:3(485)Online publication date: 24-Jan-2024
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