Towards a taxonomy of user feedback intents for conversational recommendations

Wanling Cai, Li Chen

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

4 Citations (Scopus)

Abstract

Understanding users’ feedback on recommendation in natural language is crucially important for assisting the system to refine its understanding of the user’s preferences and provide more accurate recommendations in the subsequent interactions. In this paper, we report the results of an exploratory study on a human-human dialogue dataset centered around movie recommendations. In particular, we manually labeled a set of over 200 dialogues at the utterance level, and then conducted descriptive analysis on them from both seekers’ and recommenders’ perspectives. The results reveal not only seekers’ feedback intents as well as the types of preferences they have expressed, but also the reactions of human recommenders that have finally led to successful recommendation. A taxonomy for feedback intents is established along with the results, which could be constructive for improving conversational recommender systems.

Original languageEnglish
Title of host publicationProceedings of ACM RecSys 2019 Late-breaking Results, co-located with the 13th ACM Conference on Recommender Systems (RecSys 2019)
EditorsMarko Tkalcic, Sole Pera
PublisherCEUR-WS
Pages51-55
Number of pages5
Publication statusPublished - 26 Aug 2019
Event2019 ACM Conference on Recommender Systems Late-breaking Results, ACM RecSys LBR 2019 - Copenhagen, Denmark
Duration: 16 Sept 201920 Sept 2019
https://ceur-ws.org/Vol-2431/

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR-WS
Volume2431
ISSN (Print)1613-0073

Conference

Conference2019 ACM Conference on Recommender Systems Late-breaking Results, ACM RecSys LBR 2019
Country/TerritoryDenmark
CityCopenhagen
Period16/09/1920/09/19
Internet address

Scopus Subject Areas

  • Computer Science(all)

User-Defined Keywords

  • Dialogue-based conversational recommender systems
  • Intent taxonomy
  • User feedback

Fingerprint

Dive into the research topics of 'Towards a taxonomy of user feedback intents for conversational recommendations'. Together they form a unique fingerprint.

Cite this