research-article

Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks

Authors:

Elena Smirnova,

Flavian VasileAuthors Info & Claims

DLRS 2017: Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems

Pages 2 - 9

https://doi.org/10.1145/3125486.3125488

Published: 27 August 2017 Publication History

Abstract

Recommendations can greatly benefit from good representations of the user state at recommendation time. Recent approaches that leverage Recurrent Neural Networks (RNNs) for session-based recommendations have shown that Deep Learning models can provide useful user representations for recommendation. However, current RNN modeling approaches summarize the user state by only taking into account the sequence of items that the user has interacted with in the past, without taking into account other essential types of context information such as the associated types of user-item interactions, the time gaps between events and the time of day for each interaction. To address this, we propose a new class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that can take into account the contextual information both in the input and output layers and modifying the behavior of the RNN by combining the context embedding with the item embedding and more explicitly, in the model dynamics, by parametrizing the hidden unit transitions as a function of context information. We compare our CRNNs approach with RNNs and non-sequential baselines and show good improvements on the next event prediction task.

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Cited By

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
Shuxuan C(2024)Music Recommendation System Based on Big Data and Machine Learning Algorithm2024 IEEE 4th International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB)10.1109/ICEIB61477.2024.10602611(535-538)Online publication date: 19-Apr-2024
https://doi.org/10.1109/ICEIB61477.2024.10602611
Yu PWu TWang DXu D(2024)Sequential Recommendation with Temporal Influence Based on Hawkes Process2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00042(246-253)Online publication date: 2-Jul-2024
https://doi.org/10.1109/COMPSAC61105.2024.00042
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Index Terms

Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining
      1. Data stream mining

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Information

Published In

cover image ACM Other conferences

DLRS 2017: Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems

August 2017

70 pages

ISBN:9781450353533

DOI:10.1145/3125486

Conference Chairs:
Balázs Hidasi
Gravity Research & Development
,
Alexandros Karatzoglou
Telefonica Research
,
Oren Sar-Shalom
IBM Research
,
Sander Dieleman
DeepMind
,
Bracha Shapira
Ben-Gurion University of the Negev
,
Domonkos Tikk
Gravity Research & Development

Copyright © 2017 ACM.

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IBMR: IBM Research

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 August 2017

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DLRS 2017

DLRS 2017: Workshop on Deep Learning for Recommender Systems

August 27, 2017

Como, Italy

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DLRS 2017 Paper Acceptance Rate 7 of 16 submissions, 44%;

Overall Acceptance Rate 11 of 27 submissions, 41%

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Cited By

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
Shuxuan C(2024)Music Recommendation System Based on Big Data and Machine Learning Algorithm2024 IEEE 4th International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB)10.1109/ICEIB61477.2024.10602611(535-538)Online publication date: 19-Apr-2024
https://doi.org/10.1109/ICEIB61477.2024.10602611
Yu PWu TWang DXu D(2024)Sequential Recommendation with Temporal Influence Based on Hawkes Process2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00042(246-253)Online publication date: 2-Jul-2024
https://doi.org/10.1109/COMPSAC61105.2024.00042
Kang WJeong HLee SChoi S(2024)Independent Representation of Side Information for Sequential RecommendationIEEE Access10.1109/ACCESS.2024.347697612(148516-148524)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3476976
Nahta RChauhan GMeena YGopalani D(2024)Deep learning with the generative models for recommender systems: A surveyComputer Science Review10.1016/j.cosrev.2024.10064653(100646)Online publication date: Aug-2024
https://doi.org/10.1016/j.cosrev.2024.100646
Mateos PBellogín A(2024)A systematic literature review of recent advances on context-aware recommender systemsArtificial Intelligence Review10.1007/s10462-024-10939-458:1Online publication date: 16-Nov-2024
https://doi.org/10.1007/s10462-024-10939-4
Peng YDan TZhao ZMaoliniyazi AMeng X(2024)KMIC: A Knowledge-Aware Recommendation with Multivariate Intentions Contrastive LearningWeb and Big Data10.1007/978-981-97-7235-3_6(82-98)Online publication date: 28-Aug-2024
https://doi.org/10.1007/978-981-97-7235-3_6
Liao GTang XLi TZhong LZeng P(2024)A Review of Relationship Extraction Based on Deep LearningArtificial Intelligence and Machine Learning10.1007/978-981-97-1277-9_6(73-84)Online publication date: 3-Apr-2024
https://doi.org/10.1007/978-981-97-1277-9_6
Mu RZeng XZhang J(2023)Heterogeneous information fusion based graph collaborative filtering recommendationIntelligent Data Analysis10.3233/IDA-22702527:6(1595-1613)Online publication date: 20-Nov-2023
https://doi.org/10.3233/IDA-227025
KALKAN İŞAHİN C(2023)Çapraz Satışı Destekleyebilecek Transformer ile Geliştirilmiş Bir Öneri SistemiA Transformer-Improved Recommender System Supporting Cross-SellingÇukurova Üniversitesi Mühendislik Fakültesi Dergisi10.21605/cukurovaumfd.133416638:2(571-584)Online publication date: 28-Jul-2023
https://doi.org/10.21605/cukurovaumfd.1334166
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