Personalized Content Recommendation System Using User Clustering
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Solution Overview
Problem
The electronic content publishing industry lacks effective digital solutions for providing personalized and relevant content recommendations, leading to low user engagement and revenue generation.
Innovation Solution
A system and method for generating personalized content recommendations by clustering users based on their interests, using user data such as consumption history, social data, and geographic location, and assigning tailored recommendations to user clusters, which can be delivered through various platforms like websites or mobile applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content publishers provide personalized content recommendations, then user engagement and revenue increase, but system complexity and computational resources required increase
Solution Approach 1:
The patent segments users into distinct clusters based on their interests, demographics, and behavior patterns. By dividing the user base into manageable segments (clusters with similar characteristics), the system can apply tailored recommendation strategies to each segment rather than treating all users uniformly, thus improving engagement while managing complexity through structured organization
Solution Approach 2:
The system dynamically adjusts recommendation parameters such as content selection, formatting, and delivery timing based on user cluster characteristics. By changing these parameters according to segmented user profiles, the system provides personalized recommendations at scale without requiring individually complex processing for each user
2Measurement precision
If content publishers analyze detailed user data for personalization, then recommendation relevance improves, but data processing time and computational cost increase
Solution Approach 1:
The patent performs preliminary clustering of users based on available data before the actual recommendation generation process. By pre-segmenting users into clusters and pre-processing their data characteristics, the system reduces the computational burden during real-time recommendation delivery, thus maintaining high relevance while reducing processing time
Solution Approach 2:
The system creates representative profiles or copies for each user cluster that capture the essential characteristics of all users within that cluster. Instead of processing individual user data repeatedly, the system uses these cluster representative copies to generate recommendations for multiple users simultaneously, reducing computational time while maintaining precision
Data Source
AI summary
Identifying personalized content recommendations for users in an electronic environment is disclosed. User data comprising information relating to web-based content consumption of multiple users is collected. Multiple user cluster types associated with one or more interest categories are established. A feature vector is generated for each user for each of the multiple user cluster types. Based on the generated feature vectors, the user are grouped into multiple clusters. A grade is generated for each of a plurality of candidate recommendations for each of the clusters. Based on the generated grades, one or more personalized content recommendations for each of the clusters are identified.