Target-guided knowledge-aware recommendation dialogue system: An empirical investigation

Dongding Lin, Jian Wang, Wenjie Li

Research output: Journal article publicationConference articleAcademic researchpeer-review

Abstract

The target-guided recommendation dialogue system aims to make high-quality recommendations through interactive conversations proactively and naturally. Existing methods still struggle to incorporate background knowledge for coherent response generation, and to recommend appropriate items with respect to dialogue context, user preference and recommendation target. In this paper, we investigate the problem of target-guided knowledge-aware recommendation dialogue and design a dialogue generation system to alleviate the above-mentioned issues. Specifically, we employ pre-trained language models with multi-task learning to jointly learn response generation and goal prediction towards the target. We also present a knowledge-preserving encoding strategy to maintain the facts in background knowledge. Extensive experiments on two benchmark datasets show that our system significantly outperforms various competitive models in terms of both automatic and manual evaluations. We further provide analysis and discussions to demonstrate that our system is effective in leveraging both related knowledge and planned goals to generate fluent, informative and coherent responses towards the target of recommendation.

Original languageEnglish
Pages (from-to)1-10
JournalCEUR Workshop Proceedings
Volume2960
Publication statusPublished - Oct 2021
EventJoint Workshop of the 3rd Knowledge-Aware and Conversational Recommender Systems and the 5th Recommendation in Complex Environments, KaRS-ComplexRec 2021 - Virtual, Amsterdam, Netherlands
Duration: 25 Sep 2021 → …

Keywords

  • Background knowledge
  • Multi-task learning
  • Recommendation dialogue
  • Target guiding

ASJC Scopus subject areas

  • Computer Science(all)

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