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Context-Aware Recommendations Based on Deep Learning Frameworks

Authors:

Alexander Tuzhilin,

Amit LivneAuthors Info & Claims

ACM Transactions on Management Information Systems (TMIS), Volume 11, Issue 2

Article No.: 8, Pages 1 - 15

https://doi.org/10.1145/3386243

Published: 22 May 2020 Publication History

Editorial Notes

The authors have requested minor, non-substantive changes to the VoR and, in accordance with ACM policies, a Corrected VoR was published on June 26, 2020. For reference purposes the VoR may still be accessed via the Supplemental Material section on this page.

Abstract

In this article, we suggest a novel deep learning recommendation framework that incorporates contextual information into neural collaborative filtering recommendation approaches. Since context is often represented by dynamic and high-dimensional feature space in multiple applications and services, we suggest to model contextual information in various ways for multiple purposes, such as rating prediction, generating top-k recommendations, and classification of users’ feedback. Specifically, based on the suggested framework, we propose three deep context-aware recommendation models based on explicit, unstructured, and structured latent representations of contextual data derived from various contextual dimensions (e.g., time, location, user activity). Offline evaluation on three context-aware datasets confirms that our proposed deep context-aware models surpass state-of-the-art context-aware methods. We also show that utilizing structured latent contexts in the proposed deep recommendation framework achieves significantly better performance than the other context-aware models on all datasets.

Supplementary Material

3386243_vor (3386243_vor.pdf)

Version of Record for "Context-Aware Recommendations Based on Deep Learning Frameworks" by Unger et al., ACM Transactions on Management Information Systems, Volume 11, Issue 2 (TMIS 11:2).

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Cited By

Jeong ELi XKwon APark SLi QKim J(2024)A Multimodal Recommender System Using Deep Learning Techniques Combining Review Texts and ImagesApplied Sciences10.3390/app1420920614:20(9206)Online publication date: 10-Oct-2024
https://doi.org/10.3390/app14209206
Bauman KTuzhilin AUnger M(2024)HyperCARS: Using Hyperbolic Embeddings for Generating Hierarchical Contextual Situations in Context-Aware Recommender SystemsInformation Systems Research10.1287/isre.2022.0202Online publication date: 8-Jul-2024
https://doi.org/10.1287/isre.2022.0202
Adomavicius GBauman KMobasher BTuzhilin AUnger M(2024)Workshop on Context-Aware Recommender Systems (CARS) 2024Proceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687102(1219-1221)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3687102
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Index Terms

Context-Aware Recommendations Based on Deep Learning Frameworks
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
      2. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 11, Issue 2

Research Commentary

June 2020

115 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/3398026

Editor:
Daniel Zeng
Chinese Academy of Sciences, China

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Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 May 2020

Online AM: 07 May 2020

Accepted: 01 February 2020

Revised: 01 January 2020

Received: 01 June 2019

Published in TMIS Volume 11, Issue 2

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Cited By

Jeong ELi XKwon APark SLi QKim J(2024)A Multimodal Recommender System Using Deep Learning Techniques Combining Review Texts and ImagesApplied Sciences10.3390/app1420920614:20(9206)Online publication date: 10-Oct-2024
https://doi.org/10.3390/app14209206
Bauman KTuzhilin AUnger M(2024)HyperCARS: Using Hyperbolic Embeddings for Generating Hierarchical Contextual Situations in Context-Aware Recommender SystemsInformation Systems Research10.1287/isre.2022.0202Online publication date: 8-Jul-2024
https://doi.org/10.1287/isre.2022.0202
Adomavicius GBauman KMobasher BTuzhilin AUnger M(2024)Workshop on Context-Aware Recommender Systems (CARS) 2024Proceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687102(1219-1221)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3687102
Kim DLi QJang DKim J(2024)AXCF: Aspect‐based collaborative filtering for explainable recommendationsExpert Systems10.1111/exsy.13594Online publication date: 27-Mar-2024
https://doi.org/10.1111/exsy.13594
Hussain MPal SJadidi ZFoo EKanhere S(2024)Federated Zero Trust Architecture using Artificial IntelligenceIEEE Wireless Communications10.1109/MWC.001.230040531:2(30-35)Online publication date: 11-Apr-2024
https://dl.acm.org/doi/10.1109/MWC.001.2300405
Li YLiu KSatapathy RWang SCambria E(2024)Recent Developments in Recommender Systems: A Survey [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2024.336398419:2(78-95)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1109/MCI.2024.3363984
Ayemowa MIbrahim RAdamu Bena Y(2024)A Systematic Review of the Impact of Auxiliary Information on Recommender SystemsIEEE Access10.1109/ACCESS.2024.346275012(139524-139539)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3462750
Afzal IYilmazel BKaleli C(2024)An Approach for Multi-Context-Aware Multi-Criteria Recommender Systems Based on Deep LearningIEEE Access10.1109/ACCESS.2024.342863012(99936-99948)Online publication date: 2024
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Yang SLi QLim HKim J(2024)An Attentive Aspect-Based Recommendation Model With Deep Neural NetworkIEEE Access10.1109/ACCESS.2023.334929112(5781-5791)Online publication date: 2024
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Jang DLi QLee CKim J(2024)Attention-based multi attribute matrix factorization for enhanced recommendation performanceInformation Systems10.1016/j.is.2023.102334121(102334)Online publication date: Mar-2024
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