research-article

An Attentive Interaction Network for Context-aware Recommendations

Authors:

Jian-Yun NieAuthors Info & Claims

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Pages 157 - 166

https://doi.org/10.1145/3269206.3271813

Published: 17 October 2018 Publication History

Abstract

Context-aware Recommendations (CARS) have attracted a lot of attention recently because of the impact of contextual information on user behaviors. Recent state-of-the-art methods represent the relations between users/items and contexts as a tensor, with which it is difficult to distinguish the impacts of different contextual factors and to model complex, non-linear interactions between contexts and users/items. In this paper, we propose a novel neural model, named Attentive Interaction Network (AIN), to enhance CARS through adaptively capturing the interactions between contexts and users/items. Specifically, AIN contains an Interaction-Centric Module to capture the interaction effects of contexts on users/items; a User-Centric Module and an Item-Centric Module to model respectively how the interaction effects influence the user and item representations. The user and item representations under interaction effects are combined to predict the recommendation scores. We further employ effect-level attention mechanism to aggregate multiple interaction effects. Extensive experiments on two rating datasets and one ranking dataset show that the proposed AIN outperforms state-of-the-art CARS methods. In addition, we also find that AIN provides recommendations with better explanation ability with respect to contexts than the existing approaches.

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Liu ZSheng QChu DXu XZheng HFeng K(2024)Proactive Recommendation of Composite Services in Multi-Access Edge ComputingIEEE Transactions on Services Computing10.1109/TSC.2023.332908417:2(631-644)Online publication date: Mar-2024
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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
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Index Terms

An Attentive Interaction Network for Context-aware Recommendations
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

October 2018

2362 pages

ISBN:9781450360142

DOI:10.1145/3269206

General Chair:
Alfredo Cuzzocrea
University of Trieste, Italy
,
Program Chairs:
James Allan
University of Massachusetts, USA
,
Norman Paton
University of Manchester, United Kingdom
,
Divesh Srivastava
AT&T Labs Research, USA
,
Rakesh Agrawal
Data Insights Lab, USA
,
Andrei Broder
Google Research, USA
,
Mohammed Zaki
Rensselaer Polytechnic Institute, USA
,
Selcuk Candan
Arizona State University, USA
,
Alexandros Labrinidis
University of Pittsburgh, USA
,
Assaf Schuster
Technion, Israel
,
Haixun Wang
Google Research, USA

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Published: 17 October 2018

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CIKM '18: The 27th ACM International Conference on Information and Knowledge Management

October 22 - 26, 2018

Torino, Italy

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Cited By

An YTan YSun XFerrari G(2024)Recommender System: A Comprehensive Overview of Technical Challenges and Social ImplicationsIECE Transactions on Sensing, Communication, and Control10.62762/TSCC.2024.8985031:1(30-51)Online publication date: 15-Oct-2024
https://doi.org/10.62762/TSCC.2024.898503
Liu ZSheng QChu DXu XZheng HFeng K(2024)Proactive Recommendation of Composite Services in Multi-Access Edge ComputingIEEE Transactions on Services Computing10.1109/TSC.2023.332908417:2(631-644)Online publication date: Mar-2024
https://doi.org/10.1109/TSC.2023.3329084
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
Lee SHwang S(2024)Context-aware cross feature attentive network for click-through rate predictionsApplied Intelligence10.1007/s10489-024-05659-954:19(9330-9344)Online publication date: 13-Jul-2024
https://doi.org/10.1007/s10489-024-05659-9
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
Liu DWu YLi WZhang XWang HYang QMing Z(2023)Pairwise Intent Graph Embedding Learning for Context-Aware RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608815(588-598)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608815
Sun WMa MRen PLin YChen ZRen ZMa Jde Rijke M(2023)Parallel Split-Join Networks for Shared Account Cross-Domain Sequential RecommendationsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313092735:4(4106-4123)Online publication date: 1-Apr-2023
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Unger MTuzhilin A(2023)Hierarchical Contextual Embeddings for Context-Aware Recommendations (Extended Abstract)2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00358(3863-3864)Online publication date: Apr-2023
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Ye HSong YLi MCao F(2023)A new deep graph attention approach with influence and preference relationship reconstruction for rate prediction recommendationInformation Processing & Management10.1016/j.ipm.2023.10343960:5(103439)Online publication date: Sep-2023
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Sohafi-Bonab JHosseinzadeh Aghdam MMajidzadeh K(2023)DCARS: Deep context-aware recommendation system based on session latent contextApplied Soft Computing10.1016/j.asoc.2023.110416143(110416)Online publication date: Aug-2023
https://doi.org/10.1016/j.asoc.2023.110416
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