Text Recommendation via Integrated Semantic Vectors
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Solution Overview
Problem
Existing text recommendation methods based on collaborative filtering algorithms have a low association degree between recommended and browsed texts, resulting in relatively low recommendation accuracy.
Innovation Solution
A text recommendation method that extracts feature content from a target text, processes it using multiple text analysis models to obtain semantic vectors, integrates these vectors into a single vector, and selects recommended texts based on this integrated vector, enhancing the representation capability and association degree.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If collaborative filtering algorithm is used for text recommendation, then implementation is simple, but recommendation accuracy is low
Solution Approach 1:
The patent combines multiple text analysis models (TF-IDF, Word2Vec, and neural network models) to generate integrated semantic vectors that capture both local word meanings and global text semantics. This merging of multiple analysis approaches significantly improves recommendation accuracy while maintaining implementation feasibility through modular model integration.
Solution Approach 2:
The patent creates a composite representation by integrating semantic vectors from different analysis models into a unified vector space. This composite semantic representation combines the strengths of statistical methods (TF-IDF) and deep learning approaches (Word2Vec, neural networks), achieving high recommendation accuracy without excessive implementation complexity.
2Speed
If single text analysis model is used, then processing is fast, but text representation capability is limited
Solution Approach 1:
The patent merges multiple text analysis models including TF-IDF for statistical feature extraction, Word2Vec for distributed word representations, and neural network models for semantic understanding. These models work together to generate comprehensive semantic vectors that preserve both processing efficiency and enhanced text representation capability.
Solution Approach 2:
The patent transforms text representation by creating multi-dimensional semantic vectors that capture word meanings in different contextual dimensions. By integrating vectors from multiple analysis models, the system enriches the dimensional space of text representation, enabling more accurate similarity calculations while maintaining reasonable processing speeds.
Data Source
AI summary
Embodiments of this application provide a text recommendation method performed at an electronic device. The method includes: extracting feature content of from the a target text; processing the feature content by using at least two text analysis models to obtain at least two semantic vectors; integrating the at least two semantic vectors into an integrated semantic vector of the target text; selecting, according to the integrated semantic vector and an integrated semantic vector of at least one to-be-recommended text, a recommended text corresponding to the target text from the at least one to-be-recommended text. Because the integrated semantic vector of the target text is obtained based on the at least two text analysis models, the integrated semantic vector has a stronger representing capability. When text recommendation is subsequently performed, an association degree between the recommended text and the target text can be increased, thereby improving recommendation accuracy.