Content Recommendation System Using Structural Preference Segmentation
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Solution Overview
Problem
Existing content recommendation methods based on layer-by-layer selection mechanisms fail to accurately match content supply with user preferences, leading to mismatches and unsatisfied user demands, particularly for less popular interests and diverse content needs.
Innovation Solution
Determining a user's structural preference through historical behavior data and content classification, using deep learning models to identify and prioritize recommendation content across multiple structures, ensuring a better alignment of content supply with user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a layer-by-layer selection mechanism is used for content recommendation, then the recommendation process can be systematically executed, but the matching degree between content supply and user preference deteriorates
Solution Approach 1:
The patent segments the content into multiple structural dimensions (first structure, second structure, third structure) and processes them through separate recalling, sorting, and fusing operations. This segmentation allows each structural dimension to be optimized independently while maintaining overall system efficiency, resolving the contradiction between systematic processing and precise matching.
Solution Approach 2:
The patent introduces a structural preference dimension that goes beyond traditional single-dimensional recommendation approaches. By adding structural preferences as a new dimension of analysis, the system can simultaneously maintain efficient layer-by-layer processing and achieve better content-user matching through multi-dimensional preference alignment.
2Device complexity
If traditional content recommendation methods are used, then the system remains simple to implement, but the diversity of recommended content deteriorates
Solution Approach 1:
The patent divides content into multiple structural dimensions (first structure, second structure, third structure) and applies separate recommendation logic to each. This segmentation enables diverse content recommendation while maintaining manageable system complexity through modular processing of each structural dimension.
Solution Approach 2:
The patent applies different sorting and fusion strategies to different structural dimensions based on their specific characteristics. Each structural dimension receives tailored processing (e.g., different scoring functions or fusion weights) to optimize diversity within that specific dimension while keeping the overall system architecture relatively simple.
3Speed
If content is recommended based on basic information and user behavior history, then the recalling process is fast, but the precision of user interest matching deteriorates
Solution Approach 1:
The patent performs preliminary structural classification of content into multiple dimensions before the main recommendation process. This preliminary action enables fast recalling by pre-organizing content structurally, while the subsequent sorting and fusing operations refine the user interest matching precision through multi-level processing.
Solution Approach 2:
The patent segments the recommendation process into distinct phases: recalling (fast process using basic information), sorting (precision process using structural preferences), and fusing (integration process). This segmentation allows the recalling process to maintain speed while the subsequent steps improve matching precision through structured analysis.
Data Source
AI summary
A method and apparatus for recommending a content, a device, and a medium are provided. The method may include: determining, based on historical behavior data of a user using a product and a feature of a structure of a to-be-recommended content, a target structural preference of the user, the structure being determined by classifying the to-be-recommended content based on any classifying method of a content tag system; and determining each recommendation result of the user based on the target structural preference, the recommendation result including at least two structures and a recommendation content corresponding to each structure.