Abstract: In view of the problem that the existing multi-party human-computer dialogue system is prone to ignore speakers who may be forgotten during the dialogue process, leading to weak user interaction initiative and poor user dialogue experience,a multi-party human-computer active dialogue strategy based on knowledge enhancement is proposed. The strategy uses a knowledge graph as external knowledge to learn the preferences of specific individuals who may be overlooked in the dialogue, combine the knowledge with the current group dialogue requirements to design a personalized response strategy, thereby encouraging individual active participation in group conversations. Initially, a graph attention mechanism is employed to learn the representation of interest entities specific to individuals, and a deep preference representation for individuals is obtained by introducing time-weighted aggregation. Subsequently, multiple neighbor sets are triggered along the path of the current dialogue topic knowledge subgraph to actively capture the high-level personalized interest topics of individuals. Finally, through semantic-level analysis of the current group dialogue context and requirements, the satisfaction of the group with candidate topics is evaluated, and the optimal dialogue topic is generated, which both satisfy individual preferences and consider group sentiments. Experimental results indicate that a multi-party human-computer dialogue system, which integrates external knowledge and focuses on specific speakers, effectively enhances the richness of response content and interaction satisfaction from participants, fostering continuous multi-party dialogue.

Key words: human-computer interaction ,  multi-party interaction ,  dialogue strategy ,  knowledge graph