Personalized Content Recommendation System Using Segmented Modules

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Solution Overview

Problem

Content publishers face challenges in providing personalized, trustworthy, and relevant content recommendations to users, leading to suboptimal user engagement and revenue generation.

Innovation Solution

A system and method for personalized content recommendation that utilizes user profiles, tracking cookies, and content recommendation personalization modules to analyze user behavior and preferences, generating high-quality content recommendations across multiple content networks, ensuring relevance and trustworthiness.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If content publishers provide personalized content recommendations using user profiles and tracking cookies, then user engagement and revenue are increased, but system complexity and data processing requirements increase

Engineering Contradiction:

Improveuser engagementVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system segments the recommendation process into distinct modules: user profile creation, content indexing, recommendation generation, and delivery. Each module handles specific tasks independently, reducing overall system complexity while enabling personalized recommendations that increase user engagement and revenue.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces intermediary components such as content recommendation personalization modules and candidate content recommendation pools that mediate between user profiles and final recommendations. These intermediaries simplify the complex data processing by organizing content candidates and managing the recommendation logic in discrete, manageable layers.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Manufacturing precision

If multiple sets of content recommendation candidates are identified and analyzed, then recommendation quality and relevance are improved, but processing time and computational resources increase

Engineering Contradiction:

Improverecommendation qualityVSAvoidprocessing time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-establishing user profiles, pre-indexing content, and pre-organizing candidate content recommendation pools before actual recommendation requests. This advance preparation reduces processing time during live operations while maintaining high recommendation quality through thorough preliminary analysis.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements partial action by generating multiple sets of content recommendation candidates and then selecting only the most relevant ones for final delivery. Rather than processing all possible content, the system performs excessive action in candidate generation but applies filtering to deliver a manageable, high-quality subset, balancing processing effort with recommendation precision.

Inventive Principle:

Principle #16Partial or excessive action

3Measurement precision

If user profiles and tracking cookies are used to analyze user behavior, then personalization accuracy is improved, but user privacy concerns and data security requirements increase

Engineering Contradiction:

Improvepersonalization accuracyVSAvoidprivacy concerns

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The system extracts and separates personally identifiable information from user behavior data. User profiles are created based on anonymized tracking cookies and behavioral patterns rather than direct personal information, allowing accurate personalization while reducing privacy concerns by removing sensitive data elements from the recommendation process.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS10693981B2Provisioning personalized content recommendations

Publication Date: 2020.06.23 TEADS HOLDING CO

AI summary

A personalized content recommendation provisioning method and system are described, according to various implementations. In an implementation, a user profile is established for each of multiple users within an electronic data environment (e.g., the Internet). The user profile may include information collected via a registration of the user and information identifying content consumed by the user based at least in part on information collected by an associated tracking cookie. The user profile may be used to generate a personalized grade associated with each of multiple candidate content recommendations in a high-quality candidate content recommendation pool, determine a content recommendation scope associated with a user in view of a personalized grade associated with each of the plurality of candidate content recommendations, and display the content recommendation scope to the user.