Please use this identifier to cite or link to this item: https://doi.org/10.1145/3336191.3371769
Title: Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems
Authors: Wenqiang Lei 
Xiangnan He
Yisong Miao
Qingyun Wu
Richang Hong
Min-Yen Kan 
Tat Seng Chua 
Keywords: Conversational recommendation
Dialogue system
Interactive recommendation
Recommender system
Issue Date: 3-Feb-2020
Citation: Wenqiang Lei, Xiangnan He, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan, Tat Seng Chua (2020-02-03). Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems. WSDM 2020 : 304-312. ScholarBank@NUS Repository. https://doi.org/10.1145/3336191.3371769
Abstract: Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models. A successful Conversational Recommender System (CRS) requires proper handling of interactions between conversation and recommendation. We argue that three fundamental problems need to be solved: 1) what questions to ask regarding item attributes, 2) when to recommend items, and 3) how to adapt to the users? online feedback. To the best of our knowledge, there lacks a unified framework that addresses these problems. In this work, we fill this missing interaction framework gap by proposing a new CRS framework named Estimation?Action?Reflection, or EAR, which consists of three stages to better converse with users. (1) Estimation, which builds predictive models to estimate user preference on both items and item attributes; (2) Action, which learns a dialogue policy to determine whether to ask attributes or recommend items, based on Estimation stage and conversation history; and (3) Reflection, which updates the recommender model when a user rejects the recommendations made by the Action stage. We present two conversation scenarios on binary and enumerated questions, and conduct extensive experiments on two datasets from Yelp and LastFM, for each scenario, respectively. Our experiments demonstrate significant improvements over the state-of-the-art method CRM [32], corresponding to fewer conversation turns and a higher level of recommendation hits. ? 2020 Association for Computing Machinery.
Source Title: WSDM 2020
URI: https://scholarbank.nus.edu.sg/handle/10635/167773
ISBN: 9781450368223
DOI: 10.1145/3336191.3371769
Appears in Collections:Staff Publications
Elements

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
3336191.3371769.pdf2.31 MBAdobe PDF

OPEN

NoneView/Download

SCOPUSTM   
Citations

16
checked on Nov 25, 2020

Page view(s)

76
checked on Nov 27, 2020

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.