Fetching the paper…
Reading the bibliography…
Explaining to users why some items are recommended is critical, as it can help users to make better decisions, increase their satisfaction, and gain their trust in recommender systems (RS).
Some mathematical notes on three-mode factor analysis
Ledyard R Tucker. 1966 · 1966
Earlier work this paper cites.
Analysis of individual differences in multidimensional scaling via an N-way generalization of "Eckart-Young" decomposition
J Douglas Carroll and Jih-Jie Chang. 1970 · 1970
Earlier work this paper cites.
Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work . 175–186
Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994 · 1994
Earlier work this paper cites.
A multilinear singular value decomposition
Lieven De Lathauwer, Bart De Moor, and Joos Vandewalle. 2000 · 2000
Earlier work this paper cites.
Explaining collaborative filtering recommendations. In Proceedings of the 2000 ACM conference on Computer supported cooperative work . 241–250
Jonathan L Herlocker, Joseph A Konstan, and John Riedl. 2000 · 2000
Earlier work this paper cites.
Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web . 285–295
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001 · 2001
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics . 311–318
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries. In Text summarization branches out . 74–81
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Tag recommendations in folksonomies. In European conference on principles of data mining and knowledge discovery . Springer, 506–514
Robert Jäschke, Leandro Marinho, Andreas Hotho, Lars Schmidt-Thieme, and Gerd Stumme. 2007 · 2007
Earlier work this paper cites.
Probabilistic matrix factorization. In Advances in neural information processing systems . 1257–1264
Andriy Mnih and Russ R Salakhutdinov. 2007 · 2007
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
Earlier work this paper cites.
Pairwise interaction tensor factorization for personalized tag recommendation. In Proceedings of the third ACM international conference on Web search and data mining . 81–90
Steffen Rendle and Lars Schmidt-Thieme. 2010 · 2010
Earlier work this paper cites.
Learning to rank for information retrieval
Tie-Yan Liu. 2011 · 2011
Earlier work this paper cites.
Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval . 83–92
Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma. 2014 · 2014
Earlier work this paper cites.
Who, what, when, and where: Multi-dimensional collaborative recommendations using tensor factorization on sparse user-generated data. In Proceedings of the 24th international conference on world wide web . 130–140
Preeti Bhargava, Thomas Phan, Jiayu Zhou, and Juhan Lee. 2015 · 2015
Cited alongside, same era.
Trirank: Review-aware explainable recommendation by modeling aspects. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management . 1661–1670
Xiangnan He, Tao Chen, Min-Yen Kan, and Xiao Chen. 2015 · 2015
Cited alongside, same era.
Explaining Recommendations: Design and Evaluation
Nava Tintarev and Judith Masthoff. 2015 · 2015
Cited alongside, same era.
Learning to explain entity relationships in knowledge graphs. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) . 564–574
Nikos Voskarides, Edgar Meij, Manos Tsagkias, Maarten De Rijke, and Wouter Weerkamp. 2015 · 2015
Cited alongside, same era.
Explainable recommendation through attentive multi-view learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 3622–3629
Jingyue Gao, Xiting Wang, Yasha Wang, and Xing Xie. 2019 · 2019
Later among the works it cites.
Coupled graphs and tensor factorization for recommender systems and community detection
Vassilis N Ioannidis, Ahmed S Zamzam, Georgios B Giannakis, and Nicholas D Sidiropoulos. 2019 · 2019
Later among the works it cites.
Reinforcement knowledge graph reasoning for explainable recommendation. In Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval . 285–294
Yikun Xian, Zuohui Fu, Shan Muthukrishnan, Gerard De Melo, and Yongfeng Zhang. 2019 · 2019
Later among the works it cites.
Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 329–338
Krisztian Balog and Filip Radlinski. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning to rank features for recommendation over multiple categories. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval . 305–314
Xu Chen, Zheng Qin, Yongfeng Zhang, and Tao Xu. 2016 · 2016
Cited alongside, same era.
Transnets: Learning to transform for recommendation. In Proceedings of the eleventh ACM conference on recommender systems . 288–296
Rose Catherine and William Cohen. 2017 · 2017
Cited alongside, same era.
Learning to Explain Entity Relationships by Pairwise Ranking with Convolutional Neural Networks. In IJCAI . 4018–4025
Jizhou Huang, Wei Zhang, Shiqi Zhao, Shiqiang Ding, and Haifeng Wang. 2017 · 2017
Cited alongside, same era.
Neural rating regression with abstractive tips generation for recommendation. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval . 345–354
Piji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, and Wai Lam. 2017 · 2017
Cited alongside, same era.
Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In Proceedings of the eleventh ACM conference on recommender systems . 297–305
Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu. 2017 · 2017
Cited alongside, same era.
Neural attentional rating regression with review-level explanations. In Proceedings of the 2018 World Wide Web Conference . 1583–1592
Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2018 · 2018
Cited alongside, same era.
Coevolutionary recommendation model: Mutual learning between ratings and reviews. In Proceedings of the 2018 World Wide Web Conference . 773–782
Yichao Lu, Ruihai Dong, and Barry Smyth. 2018 · 2018
Cited alongside, same era.
Explore, exploit, and explain: personalizing explainable recommendations with bandits. In Proceedings of the 12th ACM conference on recommender systems . 31–39
James McInerney, Benjamin Lacker, Samantha Hansen, Karl Higley, Hugues Bouchard, Alois Gruson, and Rishabh Mehrotra. 2018 · 2018
Cited alongside, same era.
Synthesizing aspect-driven recommendation explanations from reviews. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IICAI-20 . 2427–2434
Trung-Hoang Le, Hady W Lauw, and C Bessiere. 2020 · 2020
Later among the works it cites.
Generate neural template explanations for recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 755–764
Lei Li, Yongfeng Zhang, and Li Chen. 2020 · 2020
Later among the works it cites.
Understanding User Behavior For Document Recommendation. In Proceedings of The Web Conference 2020 . 3012–3018
Xuhai Xu, Ahmed Hassan Awadallah, Susan T. Dumais, Farheen Omar, Bogdan Popp, Robert Rounthwaite, and Farnaz Jahanbakhsh. 2020 · 2020
Later among the works it cites.
Explainable Recommendation: A Survey and New Perspectives
Yongfeng Zhang and Xu Chen. 2020 · 2020
Later among the works it cites.
Temporal meta-path guided explainable recommendation. In Proceedings of the 14th ACM international conference on web search and data mining . 1056–1064
Hongxu Chen, Yicong Li, Xiangguo Sun, Guandong Xu, and Hongzhi Yin. 2021 · 2021
Closest in time.
ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models. In Proceedings of the Web Conference 2021 . 3850–3860
Azin Ghazimatin, Soumajit Pramanik, Rishiraj Saha Roy, and Gerhard Weikum. 2021 · 2021
Closest in time.
Path-enhanced explainable recommendation with knowledge graphs
Yafan Huang, Feng Zhao, Xiangyu Gui, and Hai Jin. 2021 · 2021
Closest in time.
CAESAR: context-aware explanation based on supervised attention for service recommendations
Lei Li, Li Chen, and Ruihai Dong. 2021a · 2021
Closest in time.
Explanation as a Defense of Recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining . 1029–1037
Aobo Yang, Nan Wang, Hongbo Deng, and Hongning Wang. 2021 · 2021
Closest in time.
Graph-based Extractive Explainer for Recommendations. In Proceedings of the ACM Web Conference 2022 . 2163–2171
Peng Wang, Renqin Cai, and Hongning Wang. 2022 · 2022
Closest in time.
Comparative Explanations of Recommendations. In Proceedings of the ACM Web Conference 2022 . 3113–3123
Aobo Yang, Nan Wang, Renqin Cai, Hongbo Deng, and Hongning Wang. 2022 · 2022
Closest in time.