Context-Aware Reply Recommendation Using Segmentation and History Learning
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Solution Overview
Problem
Current technologies for reply information recommendation, such as preset information templates, word association input, and individual language models, fail to provide accurate and contextually suitable suggestions for users, especially in complex scenarios like instant messaging and social interaction.
Innovation Solution
A method and apparatus that acquire and segment user input, learn from historical text interaction history to generate a reply model, and calculate recommended reply information using semantic and fuzzy matching, along with a conditional probability model, to provide contextually appropriate suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If preset information template technology is used, then input convenience is improved, but recommendation accuracy deteriorates because only keyword matching and given rules are applied
Solution Approach 1:
The patent transforms the recommendation approach from simple keyword matching to a comprehensive parameter-based analysis including segmentation results, historical reply patterns, semantic similarity, and fuzzy matching scores. This multi-parameter evaluation system significantly improves recommendation accuracy while maintaining input convenience through automated processing.
Solution Approach 2:
The patent combines multiple recommendation strategies (template matching, historical reply retrieval, semantic matching, and fuzzy matching) into a composite recommendation system. This integrated approach leverages the strengths of each method to achieve both high accuracy and user convenience, resolving the contradiction between simple operation and precise recommendation.
2Ease of operation
If word association input technology is used, then input convenience is improved to some extent, but contextual suitability deteriorates because only word groups and phrases can be associated
Solution Approach 1:
The patent applies segmentation processing to divide the information to be replied into meaningful units (words, phrases, or sentences) based on user settings. This segmentation enables the system to analyze and match contextual elements more effectively, improving contextual suitability while maintaining input convenience through automated word-level or phrase-level association.
Solution Approach 2:
The patent extends the recommendation dimension from simple word association to multi-dimensional analysis including semantic similarity, historical context, and fuzzy matching. This dimensional expansion allows the system to provide contextually suitable recommendations beyond basic word groups and phrases, enhancing adaptability while preserving ease of use.
3Measurement precision
If individual language model is used, then input forecast and correction are performed, but contextual reply recommendation deteriorates because only user input forecast is provided
Solution Approach 1:
The patent merges the individual language model's input forecast capability with historical reply pattern retrieval and semantic matching. This combination allows the system to not only forecast user input accurately but also recommend contextually suitable replies by integrating multiple information sources, thereby improving both forecast accuracy and contextual adaptability.
Solution Approach 2:
The patent incorporates feedback mechanisms by analyzing historical reply information and user selection patterns. The system learns from past interactions to improve future recommendations, enabling it to provide both accurate input forecasts and contextually appropriate reply suggestions based on accumulated user behavior data.
4Ease of operation
If fuzzy matching technology is used, then input convenience is improved, but contextual reply information recommendation deteriorates because none can provide recommended reply information suitable for context
Solution Approach 1:
The patent creates a universal recommendation system that performs multiple functions: template matching, historical reply retrieval, semantic similarity analysis, and fuzzy matching. This multi-functional system can provide contextually suitable reply recommendations across various input scenarios while maintaining input convenience, resolving the limitation of fuzzy matching technology.
Solution Approach 2:
The patent enhances fuzzy matching by introducing additional parameters such as semantic similarity scores, historical reply frequencies, and contextual relevance weights. This parameter expansion transforms basic fuzzy matching into a sophisticated contextual recommendation system that maintains ease of use while providing accurate, context-appropriate reply suggestions.
Data Source
AI summary
A reply information recommendation method and apparatus provides recommended reply information suitable for a context that can be quickly and accurately calculated when a user replies to information. A specific solution is: acquiring information to be replied to received by a user and pre-reply information that is input by the user and corresponding to the information to be replied to; performing segmentation processing on the information to be replied to, to obtain a segmentation processing result; learning a stored text interaction history set of the user to obtain a reply model; obtaining candidate reply information with reference to the segmentation processing result of the information to be replied to and the reply model; and calculating a set of recommended reply information with reference to the candidate reply information and the pre-reply information. The embodiments of present invention are used for reply information recommendation.