Fetching the paper…
Reading the bibliography…
Explanations in conventional recommender systems have demonstrated benefits in helping the user understand the rationality of the recommendations and improving the system's efficiency, transparency, and trustworthiness.
Construction and Validation of a Scale to Measure Celebrity Endorsers’ Perceived Expertise, Trustworthiness, and Attractiveness
Roobina Ohanian. 1990 · 1990
Earlier work this paper cites.
A Scrutable Adaptive Hypertext. In AH . 384–387
Marek Czarkowski and Judy Kay. 2002 · 2002
Earlier work this paper cites.
Is seeing believing?: how recommender system interfaces affect users’ opinions. In CHI . 585–592
Dan Cosley, Shyong K. Lam, I Edwin Albert, Joseph A. Konstan, and John Riedl. 2003 · 2003
Earlier work this paper cites.
Interfaces for Eliciting New User Preferences in Recommender Systems. In UM . 178–187
Sean M. McNee, Shyong K. Lam, Joseph A. Konstan, and John Riedl. 2003 · 2003
Earlier work this paper cites.
Explaining Recommendations: Satisfaction vs. Promotion. In IUI . 153
Mustafa Bilgic and Raymond J. Mooney. 2005 · 2005
Earlier work this paper cites.
An Empirical Study on Consumer Behavior in the Interaction with Knowledge-based Recommender Applications. In CEC/EEE . 37–37
Alexander Felfernig and Bartosz Gula. 2006 · 2006
Earlier work this paper cites.
The effects of transparency on trust in and acceptance of a content-based art recommender
Henriette Cramer, Vanessa Evers, Satyan Ramlal, Maarten van Someren, Lloyd Rutledge, Natalia Stash, Lora Aroyo, and Bob J. Wielinga. 2008 · 2008
Earlier work this paper cites.
Justified Recommendations based on Content and Rating Data. In WebKDD
Panagiotis Symeonidis, Alexandros Nanopoulos, and Yannis Manolopoulos. 2008 · 2008
Earlier work this paper cites.
Over- and underestimation in different product domains. In ECAI . 14–19
Nava Tintarev and Judith Masthoff. 2008 · 2008
Earlier work this paper cites.
Do social explanations work?: studying and modeling the effects of social explanations in recommender systems. In WWW . 1133–1144
Amit Sharma and Dan Cosley. 2013 · 2013
Earlier work this paper cites.
How should I explain? A comparison of different explanation types for recommender systems
Fatih Gedikli, D. Jannach, and Mouzhi Ge. 2014 · 2014
Earlier work this paper cites.
Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In SIGIR . 83–92
Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma. 2014 · 2014
Earlier work this paper cites.
Explaining Recommendations: Design and Evaluation
Nava Tintarev and Judith Masthoff. 2015 · 2015
Earlier work this paper cites.
Crowd-Based Personalized Natural Language Explanations for Recommendations. In RecSys . 175–182
Shuo Chang, F. Maxwell Harper, and Loren G. Terveen. 2016 · 2016
Earlier work this paper cites.
Neural Rating Regression with Abstractive Tips Generation for Recommendation. In SIGIR . 345–354
Piji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, and Wai Lam. 2017 · 2017
Cited alongside, same era.
Towards Deep Conversational Recommendations. In NeurIPS . 9748–9758
Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, and Chris Pal. 2018 · 2018
Cited alongside, same era.
Explainable Recommendation: A Survey and New Perspectives
Yongfeng Zhang and Xu Chen. 2018 · 2018
Cited alongside, same era.
OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs. In ACL . 845–854
Seungwhan Moon, Pararth Shah, Anuj Kumar, and Rajen Subba. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
Towards explainable conversational recommendation. In IJCAI . 2994–3000
Zhongxia Chen, Xiting Wang, Xing Xie, Mehul Parsana, Akshay Soni, Xiang Ao, and Enhong Chen. 2021 · 2021
Later among the works it cites.
Advances and Challenges in Conversational Recommender Systems: A Survey
Chongming Gao, Wenqiang Lei, Xiangnan He, M. de Rijke, and Tat-Seng Chua. 2021 · 2021
Later among the works it cites.
Extra: Explanation ranking datasets for explainable recommendation. In SIGIR . 2463–2469
Lei Li, Yongfeng Zhang, and Li Chen. 2021 · 2021
Later among the works it cites.
RevCore: Review-Augmented Conversational Recommendation. In ACL . 1161–1173
Yu Lu, Junwei Bao, Yan Song, Zichen Ma, Shuguang Cui, Youzheng Wu, and Xiaodong He. 2021 · 2021
Later among the works it cites.
Simulating User Satisfaction for the Evaluation of Task-oriented Dialogue Systems. In SIGIR . 2499–2506
Weiwei Sun, Shuo Zhang, Krisztian Balog, Zhaochun Ren, Pengjie Ren, Zhumin Chen, and Maarten de Rijke. 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations. In SIGIR . 329–338
Krisztian Balog and Filip Radlinski. 2020 · 2020
Cited alongside, same era.
INSPIRED: Toward Sociable Recommendation Dialog Systems. In EMNLP . 8142–8152
Shirley Anugrah Hayati, Dongyeop Kang, Qingxiaoyang Zhu, Weiyan Shi, and Zhou Yu. 2020 · 2020
Cited alongside, same era.
A Survey on Conversational Recommender Systems
Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, and Li Chen. 2020 · 2020
Cited alongside, same era.
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. In ACL . 7871–7880
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Cited alongside, same era.
Towards Conversational Recommendation over Multi-Type Dialogs. In ACL . 1036–1049
Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, and Ting Liu. 2020 · 2020
Cited alongside, same era.
Deep Critiquing for VAE-based Recommender Systems. In SIGIR . 1269–1278
Kai Luo, Hojin Yang, Ga Wu, and Scott Sanner. 2020 · 2020
Cited alongside, same era.
Exploring the Limits of Transfer Learning with a Unified Text-to-TextTransformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Cited alongside, same era.
Counterfactual Explainable Recommendation. In CIKM . 1784–1793
Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen, and Yongfeng Zhang. 2021 · 2021
Later among the works it cites.
Counterfactual Explanations for Neural Recommenders. In SIGIR . 1627–1631
Khanh Tran, Azin Ghazimatin, and Rishiraj Saha Roy. 2021 · 2021
Later among the works it cites.
Xu Chen, Yongfeng Zhang, and Jingxuan Wen. 2022 · 2022
Later among the works it cites.
Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, S. Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Wei Yu, Vincent Zhao, Yanping Huang, Andrew M. Dai, Hongkun Yu, Slav Petrov, Ed Huai hsin Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc Le, and Jason Wei. 2022 · 2022
Later among the works it cites.
Improving Personalized Explanation Generation through Visualization. In ACL . 244–255
Shijie Geng, Zuohui Fu, Yingqiang Ge, Lei Li, Gerard de Melo, and Yongfeng Zhang. 2022 · 2022
Later among the works it cites.
ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations. In RecSys . 246–256
Alessandro B. Melchiorre, Navid Rekabsaz, Christian Ganhör, and Markus Schedl. 2022 · 2022
Later among the works it cites.
Variational Reasoning about User Preferences for Conversational Recommendation. In SIGIR . 165–175
Zhaochun Ren, Zhi Tian, Dongdong Li, Pengjie Ren, Liu Yang, Xin Xin, Huasheng Liang, M. de Rijke, and Zhumin Chen. 2022 · 2022
Later among the works it cites.
EGCR: Explanation Generation for Conversational Recommendation
Bingbing Wen, Xiaoning Bu, and Chirag Shah. 2022 · 2022
Later among the works it cites.