Conversational Recommendation Model Using Context Graphs
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Solution Overview
Problem
Current voice interaction systems face challenges in accurately understanding semantics and providing timely, relevant feedback, affecting the smoothness of human-machine conversation.
Innovation Solution
A conversational recommendation method and model that acquires historical conversation information, determines a target conversation object from a conversation target graph, and generates targeted conversation information for recommendation, using techniques like graph convolutional networks and recurrent neural networks to process semantic information and transition matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional voice interaction systems are used, then the system structure is simple, but the semantic understanding accuracy and feedback relevance are insufficient
Solution Approach 1:
The patent segments the voice interaction system into multiple specialized modules: voice input module, semantic analysis module, conversation context management module, recommendation generation module, and feedback module. Each module handles specific tasks, improving semantic understanding accuracy while keeping the overall system manageable through functional decomposition
Solution Approach 2:
The patent introduces a conversation context graph as an intermediary data structure that mediates between voice input and recommendation generation. This graph maintains conversation history, entity relationships, and context information, enabling accurate semantic understanding and relevant feedback without requiring direct complex processing between all system components
2Reliability
If comprehensive conversation analysis is performed, then the recommendation relevance improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary processing by maintaining a conversation context graph that continuously updates with conversation history and entity relationships during the interaction. This pre-computed context information is readily available when generating recommendations, eliminating the need for re-analyzing entire conversation histories and reducing processing time while maintaining recommendation relevance
Solution Approach 2:
The patent applies local quality by focusing computational resources on analyzing only the relevant portions of conversation context needed for current recommendation generation. The conversation context graph allows selective querying of specific entities and relationships rather than processing all conversation data uniformly, improving efficiency while maintaining recommendation quality
Data Source
AI summary
A conversational recommendation method, a method of training a conversational recommendation model, an electronic device, and a storage medium are provided, which are related to a technical field of data processing, in particular to technical fields of voice interaction, deep learning, artificial intelligence and the like. The conversational recommendation method includes: acquiring a historical conversation information; determining a target conversation object to be generated, from a conversation target graph based on the historical conversation information, the conversation target graph includes an object node, the object node is configured to represent a conversation object, and the target conversation object is determined based on the object node; and generating a target conversation information for recommendation based on the target conversation object.