{"type":"ScholarlyArticle","title":"Interacting with Explanations through Critiquing","authors":[{"name":"Diego Antognini","slug":"diego-antognini","given":"Diego","family":"Antognini","affiliation":"École Polytechnique Fédérale de Lausanne , Switzerland"},{"name":"Claudiu Musat","slug":"claudiu-musat","given":"Claudiu","family":"Musat","affiliation":"Swisscom , Switzerland"},{"name":"Boi Faltings","slug":"boi-faltings","given":"Boi","family":"Faltings","affiliation":"École Polytechnique Fédérale de Lausanne , Switzerland"}],"abstract":"Using personalized explanations to support recommendations has been shown to increase trust and perceived quality. However, to actually obtain better recommendations, there needs to be a means for users to modify the recommendation criteria by interacting with the explanation. We present a novel technique using aspect markers that learns to generate personalized explanations of recommendations from review texts, and we show that human users significantly prefer these explanations over those produced by state-of-the-art techniques.\r\n\r\nOur work's most important innovation is that it allows users to react to a recommendation by critiquing the textual explanation: removing (symmetrically adding) certain aspects they dislike or that are no longer relevant (symmetrically that are of interest). The system updates its user model and the resulting recommendations according to the critique. This is based on a novel unsupervised critiquing method for single- and multi-step critiquing with textual explanations. Empirical results show that our system achieves good performance in adapting to the preferences expressed in multi-step critiquing and generates consistent explanations.","doi":"10.24963/ijcai.2021/72","url":"https://ijcai.org/proceedings/2021/72","pdf":"https://ijcai.org/proceedings/2021/0072.pdf","markdown":"https://ijcai.org/proceedings/2021/72.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/72","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"AI Ethics, Trust, Fairness","track":"Main Track","number":72,"pages":{"first":515,"last":521},"keywords":["AI Ethics, Trust, Fairness: Explainability","Humans and AI: Personalization and User Modeling","Data Mining: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Antognini and Faltings, 2020] Diego Antognini and Boi Faltings. Hotelrec: a novel very large-scale hotel recommendation dataset. In the Language Resources and Evaluation Conference, 2020.","title":"Diego Antognini and Boi Faltings. Hotelrec: a novel very large-scale hotel recommendation dataset","authors":"Faltings Antognini","year":"2020","venue":"the Language Resources and Evaluation Conference","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Antognini et al., 2021] Diego Antognini, Claudiu Musat, and Boi Faltings. Multi-dimensional explanation of target variables from documents. Proceedings of the AAAI Conference on Artificial Intelligence, 35, 2021.","title":"Multi-dimensional explanation of target variables from documents","authors":"Antognini","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Burke et al., 1996] Robin D. Burke, Kristian J. Hammond, and Benjamin C. Young. Knowledge-based navigation of complex information spaces. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, page 462-468, 1996.","title":"Knowledge-based navigation of complex information spaces","authors":"Robin D Burke, Kristian J Hammond, Benjamin C Young","year":"1996","venue":"Proceedings of the Thirteenth National Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Catherine and Cohen, 2017] Rose Catherine and William Cohen. Transnets: Learning to transform for recommendation. In Pro- ceedings of the ACM conference on recommender systems, 2017.","title":"Transnets: Learning to transform for recommendation","authors":"Catherine, Cohen, Rose Catherine, William Cohen","year":"2017","venue":"Proceedings of the ACM conference on recommender systems","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen and Pu, 2012] Li Chen and Pearl Pu. Critiquing-based rec- ommenders: survey and emerging trends. User Modeling and User-Adapted Interaction, 22(1-2), 2012.","title":"Critiquing-based recommenders: survey and emerging trends","authors":"Chen, Pu ; ] Li, Chen, Pearl Pu","year":"2012","venue":"User Modeling and User-Adapted Interaction","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chen et al., 2019] Zhongxia Chen, Xiting Wang, Xing Xie, Tong Wu, Guoqing Bu, Yining Wang, and Enhong Chen. Co-attentive multi-task learning for explainable recommendation. In IJCAI, pages 2137-2143, 2019.","title":"Co-attentive multi-task learning for explainable recommendation","authors":"Zhongxia Chen, Xiting Wang, Xing Xie, Tong Wu, Guoqing Bu, Yining Wang, Enhong Chen","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/296"},{"position":7,"raw":"[Chen et al., 2020] Zhongxia Chen, Xiting Wang, Xing Xie, Mehul Parsana, Akshay Soni, Xiang Ao, and Enhong Chen. Towards explainable conversational recommendation. IJCAI, 2020.","title":"Towards explainable conversational recommendation","authors":"Zhongxia Chen, Xiting Wang, Xing Xie, Mehul Parsana, Akshay Soni, Xiang Ao, Enhong Chen","year":"2020","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2020/414"},{"position":8,"raw":"[Dong et al., 2017] Li Dong, Shaohan Huang, Furu Wei, Mirella Lapata, Ming Zhou, and Ke Xu. Learning to generate product reviews from attributes. In the Conference of the European Asso- ciation for Computational Linguistics, pages 623-632, 2017.","title":"Learning to generate product reviews from attributes","authors":"Li Dong, Shaohan Huang, Furu Wei, Mirella Lapata, Ming Zhou, Ke Xu","year":"2017","venue":"the Conference of the European Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[He et al., 2017] Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. Neural collaborative filter- ing. In Proceedings of the 26th international conference on world wide web, pages 173-182, 2017.","title":"Neural collaborative filtering","authors":"He","year":"2017","venue":"Proceedings of the 26th international conference on world wide web","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Kunkel et al., 2018] Johannes Kunkel, Tim Donkers, Catalin- Mihai Barbu, and Jürgen Ziegler. Trust-related effects of exper- tise and similarity cues in human-generated recommendations. In 2nd Workshop on Theory-Informed User Modeling, 2018.","title":"Trust-related effects of expertise and similarity cues in human-generated recommendations","authors":"Johannes Kunkel, Tim Donkers, Catalin-Mihai Barbu, Jürgen Ziegler","year":"2018","venue":"2nd Workshop on Theory-Informed User Modeling","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kunkel et al., 2019] Johannes Kunkel, Tim Donkers, Lisa Michael, Catalin-Mihai Barbu, and Jürgen Ziegler. Let me explain: Impact of personal and impersonal explanations on trust in recommender systems. In Proceedings of the CHI Conference on Human Factors in Computing Systems, 2019.","title":"Let me explain: Impact of personal and impersonal explanations on trust in recommender systems","authors":"Johannes Kunkel, Tim Donkers, Lisa Michael, Catalin-Mihai Barbu, Jürgen Ziegler","year":"2019","venue":"Proceedings of the CHI Conference on Human Factors in Computing Systems","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Li et al., 2017] Piji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, and Wai Lam. Neural rating regression with abstractive tips gen- eration for recommendation. In the ACM SIGIR conference on Research and Development in Information Retrieval, 2017.","title":"Neural rating regression with abstractive tips generation for recommendation","authors":"Li","year":"2017","venue":"the ACM SIGIR conference on Research and Development in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Li et al., 2019] Piji Li, Zihao Wang, Lidong Bing, and Wai Lam. Persona-aware tips generation. In The World Wide Web Confer- ence, 2019.","title":"Persona-aware tips generation","authors":"Li","year":"2019","venue":"The World Wide Web Conference","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Luo et al., 2020] Kai Luo, Scott Sanner, Ga Wu, Hanze Li, and Hojin Yang. Latent linear critiquing for conversational recom- mender systems. In Proceedings of the 29th International Con- ference on the World Wide Web, 2020.","title":"Latent linear critiquing for conversational recommender systems","authors":"Kai Luo, Scott Sanner, Ga Wu, Hanze Li, Hojin Yang","year":"2020","venue":"Proceedings of the 29th International Conference on the World Wide Web","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Mann and Thompson, 1988] William C Mann and Sandra A Thompson. Rhetorical structure theory: Toward a functional the- ory of text organization. Text-interdisciplinary Journal for the Study of Discourse, 8(3):243-281, 1988.","title":"Rhetorical structure theory: Toward a functional theory of text organization","authors":"C William, Sandra A Mann, Thompson","year":"1988","venue":"Text-interdisciplinary Journal for the Study of Discourse","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[McAuley and Leskovec, 2013] Julian McAuley and Jure Leskovec. Hidden factors and hidden topics: understand- ing rating dimensions with review text. In the ACM conference on Recommender systems, pages 165-172. ACM, 2013.","title":"Hidden factors and hidden topics: understanding rating dimensions with review text","authors":"Leskovec Mcauley, Julian Mcauley, Jure Leskovec","year":"2013","venue":"the ACM conference on Recommender systems","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Musat and Faltings, 2015] Claudiu Cristian Musat and Boi Falt- ings. Personalizing product rankings using collaborative filtering on opinion-derived topic profiles. In Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015.","title":"Personalizing product rankings using collaborative filtering on opinion-derived topic profiles","authors":"Claudiu Cristian, Musat, Boi Faltings","year":"2015","venue":"Twenty-Fourth International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Ni and McAuley, 2018] Jianmo Ni and Julian McAuley. Person- alized review generation by expanding phrases and attending on aspect-aware representations. In Proceedings of the Association for Computational Linguistics, pages 706-711, 2018.","title":"Jianmo Ni and Julian McAuley. Personalized review generation by expanding phrases and attending on aspect-aware representations","authors":"Mcauley Ni","year":"2018","venue":"Proceedings of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Ni et al., 2019] Jianmo Ni, Jiacheng Li, and Julian McAuley. Justi- fying recommendations using distantly-labeled reviews and fine- grained aspects. In the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP-IJCNLP), 2019.","title":"Justifying recommendations using distantly-labeled reviews and finegrained aspects","authors":"Ni","year":"2019","venue":"the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP-IJCNLP)","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Pu et al., 2006] Pearl Pu, Paolo Viappiani, and Boi Faltings. In- creasing user decision accuracy using suggestions. In Proceed- ings of the SIGCHI conference on Human Factors in computing systems, pages 121-130, 2006.","title":"Pearl Pu, Paolo Viappiani, and Boi Faltings. Increasing user decision accuracy using suggestions","authors":"Pu","year":"2006","venue":"Proceedings of the SIGCHI conference on Human Factors in computing systems","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Radford et al., 2019] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language mod- els are unsupervised multitask learners. OpenAI blog, 2019.","title":"Language models are unsupervised multitask learners","authors":"Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever","year":"2019","venue":"OpenAI blog","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Reilly et al., 2005] James Reilly, Kevin McCarthy, Lorraine McGinty, and Barry Smyth. Explaining compound critiques. Ar- tificial Intelligence Review, 2005.","title":"Explaining compound critiques","authors":"James Reilly, Kevin Mccarthy, Lorraine Mcginty, Barry Smyth","year":"2005","venue":"Artificial Intelligence Review","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Sun et al., 2020] Peijie Sun, Le Wu, Kun Zhang, Yanjie Fu, Richang Hong, and Meng Wang. Dual learning for explainable recommendation: Towards unifying user preference prediction and review generation. In Proceedings of World Wide Web, 2020.","title":"Dual learning for explainable recommendation: Towards unifying user preference prediction and review generation","authors":"Peijie Sun, Le Wu, Kun Zhang, Yanjie Fu, Richang Hong, Meng Wang","year":"2020","venue":"Proceedings of World Wide Web","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Tintarev and Masthoff, 2015] Nava Tintarev and Judith Masthoff. Explaining recommendations: Design and evaluation. In Recom- mender systems handbook, pages 353-382. Springer, 2015.","title":"Explaining recommendations: Design and evaluation","authors":"Nava Tintarev, Judith Masthoff","year":"2015","venue":"Recommender systems handbook","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Wang et al., 2019] Ke Wang, Hang Hua, and Xiaojun Wan. Con- trollable unsupervised text attribute transfer via editing entangled latent representation. In Annual Conference on Neural Informa- tion Processing Systems, pages 11034-11044, 2019.","title":"Controllable unsupervised text attribute transfer via editing entangled latent representation","authors":"Ke Wang, Hang Hua, Xiaojun Wan","year":"2019","venue":"Annual Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Williams and Tou, 1982] Michael D Williams and Frederich N Tou. Rabbit: an interface for database access. In Proceedings of the ACM Conference, pages 83-87, 1982.","title":"Rabbit: an interface for database access","authors":"D Michael, Frederich N Williams, Tou","year":"1982","venue":"Proceedings of the ACM Conference","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Wu et al., 2019] Ga Wu, Kai Luo, Scott Sanner, and Harold Soh. Deep language-based critiquing for recommender systems. In Proceedings of the 13th ACM Conference on Recommender Sys- tems, pages 137-145, 2019.","title":"Deep language-based critiquing for recommender systems","authors":"Ga Wu, Kai Luo, Scott Sanner, Harold Soh","year":"2019","venue":"Proceedings of the 13th ACM Conference on Recommender Systems","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Yao et al., 2019] Lili Yao, Nanyun Peng, Ralph Weischedel, Kevin Knight, Dongyan Zhao, and Rui Yan. Plan-and-write: Towards better automatic storytelling. In Proceedings of the AAAI Con- ference on Artificial Intelligence, pages 7378-7385, 2019.","title":"Plan-and-write: Towards better automatic storytelling","authors":"Lili Yao, Nanyun Peng, Ralph Weischedel, Kevin Knight, Dongyan Zhao, Rui Yan","year":"2019","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Zhang and Curley, 2018] Jingjing Zhang and Shawn P Curley. Ex- ploring explanation effects on consumers' trust in online recom- mender agents. International Journal of Human-Computer In- teraction, 34(5):421-432, 2018.","title":"Exploring explanation effects on consumers' trust in online recommender agents","authors":"Jingjing Zhang, Shawn P Curley","year":"2018","venue":"International Journal of Human-Computer Interaction","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Zhang et al., 2014] Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma. Explicit factor mod- els for explainable recommendation based on phrase-level senti- ment analysis. In the 37th ACM SIGIR conference on Research & development in information retrieval, pages 83-92, 2014.","title":"Explicit factor models for explainable recommendation based on phrase-level sentiment analysis","authors":"Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, Shaoping Ma","year":"2014","venue":"the 37th ACM SIGIR conference on Research & development in information retrieval","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Zhang et al., 2020] Tianyi Zhang, Varsha Kishore, Felix Wu, Kil- ian Q. Weinberger, and Yoav Artzi. Bertscore: Evaluating text generation with bert. In ICLR 2020, 2020.","title":"Bertscore: Evaluating text generation with bert","authors":"Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, Yoav Artzi","year":"2020","venue":"ICLR 2020","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}