Content Recommendation Model Using Historical Interaction Analysis

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Solution Overview

Problem

Content creation platforms provide homogenized content materials lacking timely updates and guidance, leading to low creative inspiration and inefficient content creation due to poor usability and inability to meet diverse user needs.

Innovation Solution

A content recommendation method and apparatus that utilizes a content recommendation model to analyze historically delivered content and interaction information to provide personalized content suggestions, including analysis information, to stimulate creative inspiration and improve usability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If content materials are provided in a standardized and unified manner, then the platform operation is simplified and management is easier, but the content becomes homogenized and lacks timeliness, leading to low creative inspiration

Engineering Contradiction:

Improveplatform operation simplicityVSAvoidcontent diversity and timeliness

Core Design Contradiction:

Ease of manufactureVSAdaptability or versatility

Solution Approach 1:

The patent implements dynamic content recommendation by transitioning from static standardized content provision to dynamic personalized recommendation. The system continuously updates and adjusts content recommendations based on real-time user behavior data, historical interaction records, and changing user preferences, making the content provision system adaptive and timely while maintaining operational simplicity through automated algorithms

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent applies local quality by providing different content recommendations to different users based on their individual characteristics and preferences. Instead of uniform content for all users, the system tailors content quality and relevance to each user's specific needs, creating localized content experiences that enhance creative inspiration while the centralized recommendation engine maintains operational efficiency

Inventive Principle:

Principle #3Local quality

2Quantity of substance

If comprehensive content materials are provided to all users, then the content richness is improved, but the cognitive threshold for users increases and usability decreases

Engineering Contradiction:

Improvecontent richnessVSAvoidusability and cognitive threshold

Core Design Contradiction:

Quantity of substanceVSEase of operation

Solution Approach 1:

The patent extracts and presents only the most relevant content materials for each user from the comprehensive content database. The recommendation system filters out unnecessary content and extracts high-value materials that match user preferences and needs, reducing the cognitive load while maintaining content richness through selective presentation of curated content

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system performs preliminary analysis and filtering of content materials before presentation to users. By pre-processing content to match user profiles and preferences in advance, the system reduces the cognitive threshold at the point of interaction, making comprehensive content accessible through pre-organized and personalized recommendations

Inventive Principle:

Principle #10Preliminary action

3Manufacturing precision

If manual content selection and curation is performed, then content quality and relevance are improved, but the time consumption and operational complexity increase

Engineering Contradiction:

Improvecontent quality and relevanceVSAvoidtime consumption for content curation

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The patent implements self-service content curation through automated recommendation algorithms that independently analyze user data, evaluate content materials, and generate personalized recommendations without manual intervention. The system serves itself by automatically updating recommendations based on changing user preferences and interaction patterns, maintaining high content quality while eliminating time-consuming manual curation operations

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system incorporates feedback mechanisms where user interactions with recommended content are continuously monitored and fed back into the recommendation algorithm. This feedback loop enables automated refinement of content quality and relevance over time, with the system learning from user behavior to improve recommendations without requiring manual content evaluation, thus maintaining precision while reducing time investment

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250211826A1Content recommendation method, storage medium and electronic device

Publication Date: 2025.06.26 BEIJING YOUZHUJU NETWORK TECH CO LTD

AI summary

A content recommendation method, a storage medium and an electronic device are provided. The content recommendation method includes: receiving a content request sent by a terminal device, wherein the content request carries target information for matching with recommended contents, and the target information includes attribute information of a historically delivered content and/or historical interaction information in a content material page displayed by the terminal device; obtaining a target recommended content according to the target information and a content recommendation model, wherein the target recommended content includes a target content material and analysis information for the target content material, and the content recommendation model is used for matching with recommended contents according to inputted information; and pushing the target recommended content to the terminal device such that the terminal device displays the target recommended content in the content material page.