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DKN: Deep Knowledge-Aware Network for News Recommendation [article]

Hongwei Wang, Fuzheng Zhang, Xing Xie, Minyi Guo
2018 arXiv   pre-print
To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation.  ...  DKN is a content-based deep recommendation framework for click-through rate prediction.  ...  for news recommendation, namely the deep knowledge-aware network (DKN).  ... 
arXiv:1801.08284v2 fatcat:c6p7njibivfsricpgrxin2nj2u

KRED: Knowledge-Aware Document Representation for News Recommendations [article]

Danyang Liu, Jianxun Lian, Shiyin Wang, Ying Qiao, Jiun-Hung Chen, Guangzhong Sun, Xing Xie
2020 arXiv   pre-print
We find that incorporating knowledge entities for better document understanding benefits these applications consistently.  ...  An industrial news recommender system contains various key applications, such as personalized recommendation, item-to-item recommendation, news category classification, news popularity prediction and local  ...  In the direction of integrating knowledge graphs for news recommendation, the most related work is DKN [21] . The key component of DKN is the knowledge-aware convolutional neural network (KCNN).  ... 
arXiv:1910.11494v2 fatcat:bmlfqk2vwng5hd65df55hpfsdy

Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation

Hongwei Wang, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, Minyi Guo
2019 The World Wide Web Conference on - WWW '19  
MKR is a deep end-to-end framework that utilizes knowledge graph embedding task to assist recommendation task.  ...  In this paper, we consider knowledge graphs as the source of side information. We propose MKR, a Multi-task feature learning approach for Knowledge graph enhanced Recommendation.  ...  Deep Knowledge-aware Network (DKN) [32] designs a CNN framework to combine entity embeddings with word embeddings for news recommendation.  ... 
doi:10.1145/3308558.3313411 dblp:conf/www/WangZZLXG19 fatcat:db4ocee6pfcgpb4dxoxxce3id4

Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation [article]

Hongwei Wang, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, Minyi Guo
2019 arXiv   pre-print
MKR is a deep end-to-end framework that utilizes knowledge graph embedding task to assist recommendation task.  ...  In this paper, we consider knowledge graphs as the source of side information. We propose MKR, a Multi-task feature learning approach for Knowledge graph enhanced Recommendation.  ...  Deep Knowledge-aware Network (DKN) [32] designs a CNN framework to combine entity embeddings with word embeddings for news recommendation.  ... 
arXiv:1901.08907v1 fatcat:ivwxqjp6anamffimhu3sohptwa

RippleNet

Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, Minyi Guo
2018 Proceedings of the 27th ACM International Conference on Information and Knowledge Management - CIKM '18  
To address the limitations of existing embedding-based and path-based methods for knowledge-graph-aware recommendation, we propose Ripple Network, an end-to-end framework that naturally incorporates the  ...  knowledge graph into recommender systems.  ...  For example, Deep Knowledge-aware Network (DKN) [33] treats entity embeddings and word embeddings as different channels, then designs a CNN framework to combine them together for news recommendation.  ... 
doi:10.1145/3269206.3271739 dblp:conf/cikm/WangZWZLXG18 fatcat:424zy6hptncpxdkob56bd5os7i

Why Do We Click: Visual Impression-aware News Recommendation [article]

Jiahao Xun, Shengyu Zhang, Zhou Zhao, Jieming Zhu, Qi Zhang, Jingjie Li, Xiuqiang He, Xiaofei He, Tat-Seng Chua, Fei Wu
2021 arXiv   pre-print
with visual-semantic modeling for news recommendation.  ...  for the content-based recommenders.  ...  DKN leverages entity embeddings from knowledge graphs as external knowledge for news recommendation. • NPA [31] .  ... 
arXiv:2109.12651v1 fatcat:pcjk6p7c4rbbrgc2hovl6zyfku

On Learning Path Planning Algorithm Based on Collaborative Analysis of Learning Behavior

Zhaoyu Shou, Xianying Lu, Zhengzheng Wu, Hua Yuan, Huibing Zhang, Junli Lai
2020 IEEE Access  
a local structure similarity measurement method between the knowledge nodes of the directed learning path network.  ...  INDEX TERMS Local structural similarity on the knowledge node of the directed learning path network, similarity measure on learning behavior, concept interaction degree of knowledge points, individualized  ...  Combined with the hierarchical time series memory model and the optimization algorithm of deep learning related knowledge, the interaction model between the structure of complex network and individual  ... 
doi:10.1109/access.2020.3005793 fatcat:ufvvwath25gvpe3nk2xjvhebxu

Multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet

YueQun Wang, LiYan Dong, YongLi Li, Hao Zhang
2021 PLoS ONE  
For current knowledge graph embedding, a deep learning framework only has one embedding mode, which fails to excavate the potential information from the knowledge graph thoroughly.  ...  Ripp-MKR is a deep end-to-end framework that utilizes a knowledge graph embedding task to assist recommendation tasks.  ...  We propose Ripp-MKR, a framework utilizing KGs to assist recommender systems.  ... 
doi:10.1371/journal.pone.0251162 pmid:33989299 fatcat:vdgbnadrynaulax7svk45l56lu

MM-Rec: Multimodal News Recommendation [article]

Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
2022 arXiv   pre-print
Accurate news representation is critical for news recommendation.  ...  In addition, we propose a crossmodal candidate-aware attention network to select relevant historical clicked news for accurate user modeling by measuring the crossmodal relatedness between clicked news  ...  ; (6) DKN [22], learning news representations via a knowledge-aware CNN model; (7) DAN [27], learning news representations with two parallel CNN networks from news title and entities; (8) NAML [23], using  ... 
arXiv:2104.07407v2 fatcat:2p427r2d3fc6dbfqbqm5ghtvra

Neural News Recommendation with Topic-Aware News Representation

Chuhan Wu, Fangzhao Wu, Mingxiao An, Yongfeng Huang, Xing Xie
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
News recommendation can help users find interested news and alleviate information overload.  ...  Acknowledgments The authors would like to thank Microsoft News for providing technical support and data in the experiments, and Jiun-Hung Chen (Microsoft News) and Ying Qiao (Microsoft News) for their  ...  et al., 2018) , a neural news recommendation method which can utilize entity information in knowledge graphs via a knowledge-aware CNN; (9) TANR-basic, our basic neural news recommendation model; (10)  ... 
doi:10.18653/v1/p19-1110 dblp:conf/acl/WuWAHX19 fatcat:zuyjmouzuzc2hpx6a5qxhrpa64

Personalized News Recommendation: Methods and Challenges [article]

Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie
2022 arXiv   pre-print
Next, we introduce the public datasets and evaluation methods for personalized news recommendation.  ...  We first review the techniques for tackling each core problem in a personalized news recommender system and the challenges they face.  ...  [40] proposed a knowledge-aware news recommendation approach with hierarchical attention networks.  ... 
arXiv:2106.08934v3 fatcat:iagqsw73hrehxaxpvpydvtr26m

MRP2Rec: Exploring Multiple-step Relation Path Semantics for Knowledge Graph-Based Recommendations

Ting Wang, Daqian Shi, Zhaodan Wang, Shuai Xu, Hao Xu
2020 IEEE Access  
[17] constructed a novel neural network model called DKN for learning text embeddings combined with entity embeddings in news recommendation scenarios. Wang et al.  ...  [18] designed SHINE embedding sentiment networks, social networks, and profile networks with deep autoencoders for recommendations.  ... 
doi:10.1109/access.2020.3011279 fatcat:tbztgj6qljgsnanmmpubvufcte

Neural News Recommendation with Long- and Short-term User Representations

Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, Xing Xie
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
Personalized news recommendation is important to help users find their interested news and improve reading experience.  ...  Acknowledgement The authors would like to thank Microsoft News for providing technical support and data in the experiments, and Jiun-Hung Chen (Microsoft News) and Ying Qiao (Microsoft News) for their  ...  We also want to thank Jianqiang Huang for his help in the experiments.  ... 
doi:10.18653/v1/p19-1033 dblp:conf/acl/AnWWZLX19 fatcat:gj3pkktkvrfung75bvypee3ohi

Neural News Recommendation with Multi-Head Self-Attention

Chuhan Wu, Fangzhao Wu, Suyu Ge, Tao Qi, Yongfeng Huang, Xing Xie
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
News recommendation can help users find interested news and alleviate information overload.  ...  Acknowledgments The authors would like to thank Microsoft News for providing technical support and data in the experiments, and Jiun-Hung Chen (Microsoft News) and Ying Qiao (Microsoft News) for their  ...  ; (5) DFM (Lian et al., 2018) , deep fusion model for news recommendation; (6) DKN (Wang et al., 2018) , deep knowledge-aware network for news recommendation; (7) Conv3D (Khattar et al., 2018) , a neural  ... 
doi:10.18653/v1/d19-1671 dblp:conf/emnlp/WuWGQHX19 fatcat:uhkzxqvfave2zge5wiemmj4in4

Hierarchical Preference Hash Network for News Recommendation

Jianyong DUAN, Liangcai LI, Mei ZHANG, Hao WANG
2022 IEICE transactions on information and systems  
Personalized news recommendation is becoming increasingly important for online news platforms to help users alleviate information overload and improve news reading experience.  ...  news recommendation.  ...  We would also like to thank the anonymous reviewers for their helpful comments. We would like to thank the referees for their comments, which helped improve this paper considerably.  ... 
doi:10.1587/transinf.2021edp7034 fatcat:sqlzfpidqrcbld2xswrtegls7i
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