Learning to Ask Appropriate Questions in Conversational Recommendation
- URL: http://arxiv.org/abs/2105.04774v1
- Date: Tue, 11 May 2021 03:58:10 GMT
- Title: Learning to Ask Appropriate Questions in Conversational Recommendation
- Authors: Xuhui Ren, Hongzhi Yin, Tong Chen, Hao Wang, Zi Huang, Kai Zheng
- Abstract summary: We propose the Knowledge-Based Question Generation System (KBQG), a novel framework for conversational recommendation.
KBQG models a user's preference in a finer granularity by identifying the most relevant relations from a structured knowledge graph.
Finially, accurate recommendations can be generated in fewer conversational turns.
- Score: 49.31942688227828
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Conversational recommender systems (CRSs) have revolutionized the
conventional recommendation paradigm by embracing dialogue agents to
dynamically capture the fine-grained user preference. In a typical
conversational recommendation scenario, a CRS firstly generates questions to
let the user clarify her/his demands and then makes suitable recommendations.
Hence, the ability to generate suitable clarifying questions is the key to
timely tracing users' dynamic preferences and achieving successful
recommendations. However, existing CRSs fall short in asking high-quality
questions because: (1) system-generated responses heavily depends on the
performance of the dialogue policy agent, which has to be trained with huge
conversation corpus to cover all circumstances; and (2) current CRSs cannot
fully utilize the learned latent user profiles for generating appropriate and
personalized responses.
To mitigate these issues, we propose the Knowledge-Based Question Generation
System (KBQG), a novel framework for conversational recommendation. Distinct
from previous conversational recommender systems, KBQG models a user's
preference in a finer granularity by identifying the most relevant relations
from a structured knowledge graph (KG). Conditioned on the varied importance of
different relations, the generated clarifying questions could perform better in
impelling users to provide more details on their preferences. Finially,
accurate recommendations can be generated in fewer conversational turns.
Furthermore, the proposed KBQG outperforms all baselines in our experiments on
two real-world datasets.
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