extended-abstract

Context-aware Graph Embedding for Session-based News Recommendation

Authors:

Heng-Shiou Sheu,

Sheng LiAuthors Info & Claims

RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems

Pages 657 - 662

https://doi.org/10.1145/3383313.3418477

Published: 22 September 2020 Publication History

Abstract

Online news recommender systems aim to make personalized recommendations according to user preferences, which require modeling users’ short-term reading interest. However, due to the limited logged user interactions in practice, news recommendation at session-level becomes very challenging. Existing methods on session-based news recommendation mainly focus on extracting features from news articles and sequential user-item interactions, but they usually ignore the semantic-level structural information among news articles and do not explore external knowledge sources. In this paper, we propose a novel Context-Aware Graph Embedding (CAGE) framework for session-based news recommendation, which builds an auxiliary knowledge graph to enrich the semantic meaning of entities involved in articles, and further refines the article embeddings by graph convolutional networks. Experimental results on a real-world news dataset demonstrate the effectiveness of our method compared with the state-of-the-art methods on session-based news recommendation.

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Cited By

Iana AAlam MPaulheim H(2024)A survey on knowledge-aware news recommender systemsSemantic Web10.3233/SW-22299115:1(21-82)Online publication date: 12-Jan-2024
https://doi.org/10.3233/SW-222991
Kille BLommatzsch ATreuillier CYadav VÖzgöbek Ö(2024)12th International Workshop on News Recommendation and Analytics (INRA'24)Proceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687100(1258-1261)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3687100
Liu ZChen ZZhang MDuan SWen HLi LLi NGu YYu GChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Modeling User Viewing Flow using Large Language Models for Article RecommendationCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648305(83-92)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648305
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Index Terms

Context-aware Graph Embedding for Session-based News Recommendation
1. Computing methodologies
  1. Machine learning
2. Information systems

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems

September 2020

796 pages

ISBN:9781450375832

DOI:10.1145/3383313

Copyright © 2020 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 22 September 2020

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RecSys '20

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RecSys '20: Fourteenth ACM Conference on Recommender Systems

September 22 - 26, 2020

Virtual Event, Brazil

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Iana AAlam MPaulheim H(2024)A survey on knowledge-aware news recommender systemsSemantic Web10.3233/SW-22299115:1(21-82)Online publication date: 12-Jan-2024
https://doi.org/10.3233/SW-222991
Kille BLommatzsch ATreuillier CYadav VÖzgöbek Ö(2024)12th International Workshop on News Recommendation and Analytics (INRA'24)Proceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687100(1258-1261)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3687100
Liu ZChen ZZhang MDuan SWen HLi LLi NGu YYu GChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Modeling User Viewing Flow using Large Language Models for Article RecommendationCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648305(83-92)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648305
Ou ZHan ZLiu PTeng SSong M(2024)SIIR: Symmetrical Information Interaction Modeling for News RecommendationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.329979035:12(17111-17122)Online publication date: Dec-2024
https://doi.org/10.1109/TNNLS.2023.3299790
Fang XYang HShi LWang YLi L(2024)BERT-Based Semantic-Aware Heterogeneous Graph Embedding Method for Enhancing App Usage Prediction AccuracyIEEE Transactions on Human-Machine Systems10.1109/THMS.2024.341227354:4(465-474)Online publication date: Aug-2024
https://doi.org/10.1109/THMS.2024.3412273
Chen YChen YHsu C(2024)G-TransRec: A Transformer-Based Next-Item Recommendation With Time PredictionIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.335431511:3(4175-4188)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2024.3354315
Chen BXu YZhen JHe XFang QCao J(2024)NRMG: News Recommendation With Multiview Graph Convolutional NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.326652011:2(2245-2255)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3266520
Wang SLi YXiao HBao ZDeng LDong Y(2024)Enabling Roll-Up and Drill-Down Operations in News Exploration with Knowledge Graphs for Due Diligence and Risk Management2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00400(1-7)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00400
El Alaoui DRiffi JSabri AAghoutane BYahyaouy ATairi H(2024)Contextual Recommendations: Dynamic Graph Attention Networks With Edge AdaptationIEEE Access10.1109/ACCESS.2024.347795612(151019-151029)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3477956
Ruenin PChoetkiertikul MSupratak ATuarob S(2024)TeReKG: A temporal collaborative knowledge graph framework for software team recommendationKnowledge-Based Systems10.1016/j.knosys.2024.111492289(111492)Online publication date: Apr-2024
https://doi.org/10.1016/j.knosys.2024.111492
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