@inproceedings{galitsky-ilvovsky-2020-interrupt,
    title = "Interrupt me Politely: Recommending Products and Services by Joining Human Conversation",
    author = "Galitsky, Boris  and
      Ilvovsky, Dmitry",
    editor = "Zhao, Huasha  and
      Sondhi, Parikshit  and
      Bach, Nguyen  and
      Hewavitharana, Sanjika  and
      He, Yifan  and
      Si, Luo  and
      Ji, Heng",
    booktitle = "Proceedings of Workshop on Natural Language Processing in E-Commerce",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.ecomnlp-1.4/",
    pages = "32--42",
    abstract = "We propose a novel way of conversational recommendation, where instead of asking questions to the user to acquire their preferences; the recommender tracks their conversation with other people, including customer support agents (CSA), and joins the conversation only when it is time to introduce a recommendation. Building a recommender that joins a human conversation (RJC), we propose information extraction, discourse and argumentation analyses, as well as dialogue management techniques to compute a recommendation for a product and service that is needed by the customer, as inferred from the conversation. A special case of such conversations is considered where the customer raises his problem with CSA in an attempt to resolve it, along with receiving a recommendation for a product with features addressing this problem. We evaluate performance of RJC is in a number of human-human and human-chat bot dialogues, and demonstrate that RJC is an efficient and less intrusive way to provide high relevance and persuasive recommendations."
}