Using Dialogue Model to Guide Recommendation of Dialogue Generation

Conversational recommendation technology aims to achieve high-quality information recommendation through dialogue interaction with users. Aiming at the problem that the accuracy of dialogue goal prediction is not high, a dialogue guided recommendation of dialogue generation (DGRDG)model is proposed. Firstly, a dialogue model is used to generate the dialogue goal, and the classic Seq2Seq model is used to fuse the input dialogue history, user profile and knowledge information to generate the dialogue goal. Secondly, a goal replan policy(GRP) is proposed to modify the generated dialogue goal to improve the accuracy of dialogue goal prediction. The experimental results on DuRecDial dataset show that the accuracy of dialogue goal prediction is improved by 3. 93% after the GRP is introduced into the dialogue goal generation module. And the overall model has acquired good results in BLEU, DISTINCT, F1 and human evaluation. © 2022 Northeastern University. All rights reserved.