Sentence Recommendation via Point of Interest Association Network
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Solution Overview
Problem
Current human-machine conversation systems rely on initial user interest information that is often coarse and limited in scope, leading to a narrow coverage of topics and inefficient topic expansion.
Innovation Solution
A sentence recommendation method and apparatus that utilize a point of interest association network to identify extension points of interest based on user feedback, expanding user interests by retrieving and providing reply sentences from a preset corpus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If initial user interest information is obtained from user specification or brief interaction, then the information acquisition process is simple and fast, but the information granularity is coarse and coverage is limited
Solution Approach 1:
The system implements feedback mechanisms by analyzing user responses to recommended sentences and continuously updating the point of interest association network. User feedback on recommended content is used to refine interest profiles and expand the association network dynamically, transforming initial coarse interest information into fine-grained, comprehensive user interest representations over time
Solution Approach 2:
The system performs preliminary action by pre-construction of the point of interest association network with extensive topic relationships before actual user interaction. This pre-established knowledge structure enables the system to immediately begin expanding user interests from initial coarse information without requiring extensive real-time data collection
2Device complexity
If topic creation is wholly dependent on user autonomous switching, then the system operation is simple, but the topic coverage becomes narrow and accumulation process is inefficient
Solution Approach 1:
The system uses feedback from user interactions with recommended sentences to identify emerging interest areas and automatically introduces new topics through the point of interest association network. This feedback-driven topic introduction mechanism expands topic coverage without requiring complex manual topic management
Solution Approach 2:
The point of interest association network performs self-service by automatically expanding and updating itself based on user interactions. The system autonomously identifies extension points of interest and retrieves relevant sentences, eliminating the need for complex external topic management while continuously improving topic coverage
3Loss of information
If the point of interest association network is used to find extension points, then the diversity of topics is improved, but the system complexity increases
Solution Approach 1:
The complex point of interest association network is constructed in advance during system initialization, organizing extensive topic relationships before user interaction begins. This preliminary construction of the knowledge structure enables diverse topic expansion during operation without requiring complex real-time processing
Solution Approach 2:
The point of interest association network serves as an intermediary layer between user input and reply generation. It mediates the complexity by pre-organizing topic relationships and providing structured extension points, thereby simplifying the overall system architecture while enabling diverse topic coverage
Data Source
AI summary
The present disclosure provides a sentence recommendation method and apparatus based on associated points of interest. The method includes: obtaining an input sentence from a user; extracting a keyword in the input sentence, and searching for a current point of interest matching the keyword in a preset point of interest association network determining a plurality of associated points of interest matching the current point of interest according to the preset point of interest association network, and filtering out an extension point of interest from the plurality of associated points of interest according to a preset filter strategy; and retrieving a first reply sentence and a second reply sentence from a preset corpus according to the current point of interest and the extension point of interest, and providing them to the user.