research-article

Improved Recurrent Neural Networks for Session-based Recommendations

Authors:

Yong LiuAuthors Info & Claims

DLRS 2016: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems

Pages 17 - 22

https://doi.org/10.1145/2988450.2988452

Published: 15 September 2016 Publication History

Abstract

Recurrent neural networks (RNNs) were recently proposed for the session-based recommendation task. The models showed promising improvements over traditional recommendation approaches. In this work, we further study RNN-based models for session-based recommendations. We propose the application of two techniques to improve model performance, namely, data augmentation, and a method to account for shifts in the input data distribution. We also empirically study the use of generalised distillation, and a novel alternative model that directly predicts item embeddings. Experiments on the RecSys Challenge 2015 dataset demonstrate relative improvements of 12.8% and 14.8% over previously reported results on the Recall@20 and Mean Reciprocal Rank@20 metrics respectively.

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Wang LJin D(2024)A Time-Sensitive Graph Neural Network for Session-Based New Item RecommendationElectronics10.3390/electronics1301022313:1(223)Online publication date: 3-Jan-2024
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Improved Recurrent Neural Networks for Session-based Recommendations
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals

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Published In

cover image ACM Other conferences

DLRS 2016: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems

September 2016

47 pages

ISBN:9781450347952

DOI:10.1145/2988450

Conference Chairs:
Alexandros Karatzoglou
Telefonica Research
,
Balázs Hidasi
Gravity Research & Development
,
Domonkos Tikk
Gravity Research Development
,
Oren Sar-Shalom
IBM Research
,
Haggai Roitman
IBM Research
,
Bracha Shapira
Ben-Gurion University
,
Lior Rokach
Ben-Gurion University

Copyright © 2016 ACM.

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IBMR: IBM Research

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New York, NY, United States

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Published: 15 September 2016

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DLRS 2016

DLRS 2016: Workshop on Deep Learning for Recommender Systems

September 15, 2016

MA, Boston, USA

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Overall Acceptance Rate 11 of 27 submissions, 41%

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Cited By

Abbasi MBernardo MVáz PSilva JMartins P(2024)Revisiting Database Indexing for Parallel and Accelerated Computing: A Comprehensive Study and Novel ApproachesInformation10.3390/info1508042915:8(429)Online publication date: 24-Jul-2024
https://doi.org/10.3390/info15080429
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
https://doi.org/10.3390/electronics13030485
Wang LJin D(2024)A Time-Sensitive Graph Neural Network for Session-Based New Item RecommendationElectronics10.3390/electronics1301022313:1(223)Online publication date: 3-Jan-2024
https://doi.org/10.3390/electronics13010223
Mao XLi LHe J(2024)Multi-Granularity and Multi-Interest Contrast-Enhanced Hypergraph Convolutional Networks for Session RecommendationApplied Sciences10.3390/app1418829314:18(8293)Online publication date: 14-Sep-2024
https://doi.org/10.3390/app14188293
Celik EIlhan Omurca S(2024)Skip-Gram and Transformer Model for Session-Based RecommendationApplied Sciences10.3390/app1414635314:14(6353)Online publication date: 21-Jul-2024
https://doi.org/10.3390/app14146353
Vuong Nguyen L(2024)Classifications, evaluation metrics, datasets, and domains in recommendation services: A surveyInternational Journal of Hybrid Intelligent Systems10.3233/HIS-24000320:2(85-100)Online publication date: 11-Jun-2024
https://doi.org/10.3233/HIS-240003
Kumar AKwatra CGiri AKumar AKumar SPoudel K(2024)Anime Recommendation SystemSSRN Electronic Journal10.2139/ssrn.4491482Online publication date: 2024
https://doi.org/10.2139/ssrn.4491482
Li ZYang CChen YWang XChen HXu GYao LSheng M(2024)Graph and Sequential Neural Networks in Session-based Recommendation: A SurveyACM Computing Surveys10.1145/369641357:2(1-37)Online publication date: 18-Sep-2024
https://dl.acm.org/doi/10.1145/3696413
Wan JChi CYu HLiu YXu XLyu HWang W(2024)Fed-AttGRU Privacy-preserving Federated Interest RecommendationProceedings of the ACM Turing Award Celebration Conference - China 202410.1145/3674399.3674450(138-143)Online publication date: 5-Jul-2024
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Zhuo XQian SHu JDai FLin KWu G(2024)Multi-Hop Multi-View Memory Transformer for Session-Based RecommendationACM Transactions on Information Systems10.1145/366376042:6(1-28)Online publication date: 8-May-2024
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