Intelligent conversation recommendation method and device based on knowledge graph

Abstract

The invention provides an intelligent conversation recommendation method and device based on a knowledge graph. The method comprises the following steps: collecting voice information of a user side; the voice information is input into a trained language model, a natural language reply corresponding to the voice information is obtained, and the trained language model is obtained by training a language model through sample conversation historical content and a sample intention inference tree; wherein the sample intention reasoning tree is obtained by reasoning based on a knowledge graph according to a preset conversation intention, a sample conversation historical representation and a sample user portrait; the sample conversation history representation is obtained by encoding the sample conversation history content; the sample user portrait is obtained based on a self-attention mechanism by encoding nodes of the knowledge graph mentioned in the historical content of the sample conversation. According to the method, modeling is carried out on entity reasoning processes under different conversation intentions, so that the accuracy of intelligent conversation recommendation and the smoothness of language generation are effectively improved.

Description

technical field [0001] The present invention relates to the technical field of natural language processing, and in particular to a knowledge map-based intelligent dialogue recommendation method and device. Background technique [0002] In today's era of information explosion, large-scale Internet-based e-commerce platforms have completely changed people's lifestyles. Faced with a large amount of product information on the platform, users are often caught in the dilemma of selection and comparison. How to quickly understand user preferences and accurately recommend users' favorite products has become a difficult problem. [0003] Traditional recommendation systems require a large amount of user historical data. In cold start scenarios where historical data is missing, existing recommendation systems cannot meet the needs of accurate recommendations. Combined with the rapid development of dialogue systems in recent years, the Conversational Recommendation System (CRS) came in...

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