Article

Next News Recommendation via Knowledge-Aware Sequential Model

Authors:

Cheng ZhouAuthors Info & Claims

Chinese Computational Linguistics: 18th China National Conference, CCL 2019, Kunming, China, October 18–20, 2019, Proceedings

Pages 221 - 232

https://doi.org/10.1007/978-3-030-32381-3_18

Published: 18 October 2019 Publication History

Abstract

A news recommendation system aims to predict the next news based on users’ interaction histories. In general, the clicking sequences from the interaction histories indicate users’ latent preference, which plays an important role in predicting their future interest. Besides, news articles consist of considerable knowledge entities which have deep connections from common sense of human. In this paper, we propose a Self-Attention Sequential Knowledge-aware Recommendation (Saskr) system consisting of sequential-aware and knowledge-aware modelling. We use the self-attention mechanism to uncover sequential patterns in the sequential-aware modelling. The knowledge-aware modelling leverage the knowledge graph as side information to mine deep connections between news, thus improving diversity and extensibility of recommendation. Content-based news embeddings help to address the item cold-start problem. Through extensive experiments on the real-world news dataset, we demonstrate that the proposed model outperforms state-of-the-art deep neural sequential recommendation systems.

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Cited By

Zhu ZLi SLiu YZhang XFeng ZHou Y(2024)High-level preferences as positive examples in contrastive learning for multi-interest sequential recommendationWorld Wide Web10.1007/s11280-024-01263-627:2Online publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1007/s11280-024-01263-6
Zhou PYe QXie YGao JWang SKim JYou CKim SFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Attention Calibration for Transformer-based Sequential RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614785(3595-3605)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614785

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Information & Contributors

Information

Published In

cover image Guide Proceedings

Chinese Computational Linguistics: 18th China National Conference, CCL 2019, Kunming, China, October 18–20, 2019, Proceedings

Oct 2019

719 pages

ISBN:978-3-030-32380-6

DOI:10.1007/978-3-030-32381-3

Editors:
Maosong Sun
Tsinghua University, Beijing, China
,
Xuanjing Huang
Fudan University, Shanghai, China
,
Heng Ji
University of Illinois at Urbana Champaign, Illinois, USA
,
Zhiyuan Liu
Tsinghua University, Beijing, China
,
Yang Liu
Tsinghua University, Beijing, China

© Springer Nature Switzerland AG 2019.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 18 October 2019

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Cited By

Zhu ZLi SLiu YZhang XFeng ZHou Y(2024)High-level preferences as positive examples in contrastive learning for multi-interest sequential recommendationWorld Wide Web10.1007/s11280-024-01263-627:2Online publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1007/s11280-024-01263-6
Zhou PYe QXie YGao JWang SKim JYou CKim SFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Attention Calibration for Transformer-based Sequential RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614785(3595-3605)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614785

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