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Conversational and question-based recommender systems have gained increasing attention in recent years, with users enabled to converse with the system and better control recommendations.
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Which non-functional requirements do developers focus on? an empirical study on stack overflow using topic analysis. In 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories . IEEE, 446–449
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Chen He, Denis Parra, and Katrien Verbert. 2016 · 2016
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Learning latent vector spaces for product search. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management . ACM, 165–174
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Feipeng Zhao, Min Xiao, and Yuhong Guo. 2016 · 2016
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Converse-et-impera: Exploiting deep learning and hierarchical reinforcement learning for conversational recommender systems. In Conference of the Italian Association for Artificial Intelligence . Springer, 372–386
Claudio Greco, Alessandro Suglia, Pierpaolo Basile, and Giovanni Semeraro. 2017 · 2017
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Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web . International World Wide Web Conferences Steering Committee, 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
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Interacting with recommenders – overview and research directions
Michael Jugovac and Dietmar Jannach. 2017 · 2017
Cited alongside, same era.
Technology Assisted Reviews: Finding the Last Few Relevant Documents by Asking Yes/No Questions to Reviewers. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR ’18) . Association for Computing Machinery, New York, NY, USA, 949–952
Jie Zou, Dan Li, and Evangelos Kanoulas. 2018 · 2018
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Asking Clarifying Questions in Open-Domain Information-Seeking Conversations. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . ACM, 475–484
Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, and W Bruce Croft. 2019 · 2019
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Towards Knowledge-Based Recommender Dialog System
Qibin Chen, Junyang Lin, Yichang Zhang, Ming Ding, Yukuo Cen, Hongxia Yang, and Jie Tang. 2019 · 2019
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MusicBot: Evaluating Critiquing-Based Music Recommenders with Conversational Interaction. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management . 951–960
Yucheng Jin, Wanling Cai, Li Chen, Nyi Nyi Htun, and Katrien Verbert. 2019 · 2019
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Deep Conversational Recommender in Travel
Lizi Liao, Ryuichi Takanobu, Yunshan Ma, Xun Yang, Minlie Huang, and Tat-Seng Chua. 2019 · 2019
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Preference Elicitation Strategy for Conversational Recommender System. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining . ACM, 824–825
Bilih Priyogi. 2019 · 2019
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An Approach to Conversational Recommendation of Restaurants. In International Conference on Human-Computer Interaction . Springer, 123–130
Nicola Sardella, Claudio Biancalana, Alessandro Micarelli, and Giuseppe Sansonetti. 2019 · 2019
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Deep language-based critiquing for recommender systems. In Proceedings of the 13th ACM Conference on Recommender Systems . 137–145
Ga Wu, Kai Luo, Scott Sanner, and Harold Soh. 2019 · 2019
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An Visual Dialog Augmented Interactive Recommender System. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 157–165
Tong Yu, Yilin Shen, and Hongxia Jin. 2019 · 2019
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Learning to Ask: Question-Based Sequential Bayesian Product Search. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM ’19) . Association for Computing Machinery, New York, NY, USA, 369–378
Jie Zou and Evangelos Kanoulas. 2019 · 2019
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Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems. In Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM ’20) . Association for Computing Machinery, New York, NY, USA, 304–312
Wenqiang Lei, Xiangnan He, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan, and Tat-Seng Chua. 2020 · 2020
Closest in time.
Towards Question-Based High-Recall Information Retrieval: Locating the Last Few Relevant Documents for Technology-Assisted Reviews
Jie Zou and Evangelos Kanoulas. 2020 · 2020
Closest in time.