Personalized News Recommendation with Knowledge-aware Interactive Matching

The most important task in personalized news recommendation is accurate matching between candidate news and user interest. Most of existing news recommendation methods model candidate news from its textual content and user interest from their clicked news in an independent way. However, a news article may cover multiple aspects and entities, and a user usually has different kinds of interest. Independent modeling of candidate news and user interest may lead to inferior matching between news and users. In this paper, we propose a knowledge-aware interactive matching method for news recommendation. Our method interactively models candidate news and user interest to facilitate their accurate matching. We design a knowledge-aware news co-encoder to interactively learn representations for both clicked news and candidate news by capturing their relatedness in both semantic and entities with the help of knowledge graphs. We also design a user-news co-encoder to learn candidate news-aware user interest representation and user-aware candidate news representation for better interest matching. Experiments on two real-world datasets validate that our method can effectively improve the performance of news recommendation.

[1]  Xing Xie,et al.  PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision , 2020, FINDINGS.

[2]  Tao Qi,et al.  Clickbait Detection with Style-aware Title Modeling and Co-attention , 2020, CCL.

[3]  Xing Xie,et al.  Neural news recommendation with negative feedback , 2020, CCF Transactions on Pervasive Computing and Interaction.

[4]  Xing Xie,et al.  MIND: A Large-scale Dataset for News Recommendation , 2020, ACL.

[5]  Tao Qi,et al.  User Modeling with Click Preference and Reading Satisfaction for News Recommendation , 2020, IJCAI.

[6]  Xing Xie,et al.  Fairness-aware News Recommendation with Decomposed Adversarial Learning , 2020, AAAI.

[7]  Xing Xie,et al.  Fine-grained Interest Matching for Neural News Recommendation , 2020, ACL.

[8]  Suyu Ge,et al.  Graph Enhanced Representation Learning for News Recommendation , 2020, WWW.

[9]  Chuhan Wu,et al.  Privacy-Preserving News Recommendation Model Learning , 2020, FINDINGS.

[10]  Tao Qi,et al.  Neural News Recommendation with Heterogeneous User Behavior , 2019, EMNLP.

[11]  Suyu Ge,et al.  Neural News Recommendation with Multi-Head Self-Attention , 2019, EMNLP.

[12]  Xing Xie,et al.  KRED: Knowledge-Aware Document Representation for News Recommendations , 2019, RecSys.

[13]  Xing Xie,et al.  Hi-Fi Ark: Deep User Representation via High-Fidelity Archive Network , 2019, IJCAI.

[14]  Huan Liu,et al.  dEFEND: Explainable Fake News Detection , 2019, KDD.

[15]  Xiaofei Zhou,et al.  DAN: Deep Attention Neural Network for News Recommendation , 2019, AAAI.

[16]  Xing Xie,et al.  Neural News Recommendation with Attentive Multi-View Learning , 2019, IJCAI.

[17]  Xing Xie,et al.  NPA: Neural News Recommendation with Personalized Attention , 2019, KDD.

[18]  Xing Xie,et al.  Neural News Recommendation with Long- and Short-term User Representations , 2019, ACL.

[19]  Xing Xie,et al.  Neural News Recommendation with Topic-Aware News Representation , 2019, ACL.

[20]  Chuhan Wu,et al.  Hierarchical User and Item Representation with Three-Tier Attention for Recommendation , 2019, NAACL.

[21]  Vasudeva Varma,et al.  Weave&Rec: A Word Embedding based 3-D Convolutional Network for News Recommendation , 2018, CIKM.

[22]  Oriol Vinyals,et al.  Representation Learning with Contrastive Predictive Coding , 2018, ArXiv.

[23]  Xing Xie,et al.  Towards Better Representation Learning for Personalized News Recommendation: a Multi-Channel Deep Fusion Approach , 2018, IJCAI.

[24]  Xiaoyu Du,et al.  Adversarial Personalized Ranking for Recommendation , 2018, SIGIR.

[25]  Nicholas Jing Yuan,et al.  DRN: A Deep Reinforcement Learning Framework for News Recommendation , 2018, WWW.

[26]  Tao Zhang,et al.  Recommendation in Heterogeneous Information Networks Based on Generalized Random Walk Model and Bayesian Personalized Ranking , 2018, WSDM.

[27]  Minyi Guo,et al.  DKN: Deep Knowledge-Aware Network for News Recommendation , 2018, WWW.

[28]  Pietro Liò,et al.  Graph Attention Networks , 2017, ICLR.

[29]  Yukihiro Tagami,et al.  Embedding-based News Recommendation for Millions of Users , 2017, KDD.

[30]  Yuji Matsumoto,et al.  Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach , 2017, IJCAI.

[31]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[32]  Chiranjib Bhattacharyya,et al.  Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles , 2015, RecSys.

[33]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[34]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[35]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[36]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[37]  Jason Weston,et al.  Translating Embeddings for Modeling Multi-relational Data , 2013, NIPS.

[38]  Chong Wang,et al.  Collaborative topic modeling for recommending scientific articles , 2011, KDD.

[39]  Mária Bieliková,et al.  Content-Based News Recommendation , 2010, EC-Web.

[40]  Jiahui Liu,et al.  Personalized news recommendation based on click behavior , 2010, IUI '10.

[41]  Xiaojun Wan,et al.  Single Document Keyphrase Extraction Using Neighborhood Knowledge , 2008, AAAI.

[42]  Abhinandan Das,et al.  Google news personalization: scalable online collaborative filtering , 2007, WWW '07.

[43]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[44]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[45]  Tao Qi,et al.  SentiRec: Sentiment Diversity-aware Neural News Recommendation , 2020, AACL.

[46]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[47]  Chen Lin,et al.  Personalized news recommendation via implicit social experts , 2014, Inf. Sci..

[48]  Simon Haykin,et al.  GradientBased Learning Applied to Document Recognition , 2001 .

[49]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.