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.- Anthology ID:
- 2020.ecomnlp-1.4
- Volume:
- Proceedings of Workshop on Natural Language Processing in E-Commerce
- Month:
- Dec
- Year:
- 2020
- Address:
- Barcelona, Spain
- Editors:
- Huasha Zhao, Parikshit Sondhi, Nguyen Bach, Sanjika Hewavitharana, Yifan He, Luo Si, Heng Ji
- Venue:
- EcomNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 32–42
- Language:
- URL:
- https://aclanthology.org/2020.ecomnlp-1.4
- DOI:
- Cite (ACL):
- Boris Galitsky and Dmitry Ilvovsky. 2020. Interrupt me Politely: Recommending Products and Services by Joining Human Conversation. In Proceedings of Workshop on Natural Language Processing in E-Commerce, pages 32–42, Barcelona, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Interrupt me Politely: Recommending Products and Services by Joining Human Conversation (Galitsky & Ilvovsky, EcomNLP 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.ecomnlp-1.4.pdf