Digital Magazine Server Content Recommendation via Association Scoring
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Solution Overview
Problem
Conventional methods for recommending content in digital magazines rely on limited user interaction data, which restricts the accuracy and relevance of content suggestions to users.
Innovation Solution
A digital magazine server analyzes user interactions and associations across multiple digital magazines to generate scores for content items and magazines, ranking them based on similarity and influence, allowing for personalized content recommendations and advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods use user interaction data for content recommendation, then the system can provide personalized suggestions, but the accuracy and relevance of recommendations are limited due to insufficient data
Solution Approach 1:
The patent combines multiple data sources including user interactions with content items, user associations of content items with digital magazines, and metadata from various digital magazines to create a comprehensive recommendation system. This merging of diverse data sources increases the total data volume available for analysis, thereby improving recommendation accuracy beyond what single-source methods can achieve
Solution Approach 2:
The patent introduces a new dimension of analysis by incorporating temporal relationships between content item associations across different digital magazines. By analyzing when users associated content items with specific digital magazines and comparing this timing with subsequent interactions, the system creates a temporal dimension that enriches the data landscape and improves recommendation precision
2Measurement precision
If the system analyzes multiple digital magazines and their associations, then recommendation relevance improves, but system complexity increases
Solution Approach 1:
The patent segments the complex analysis task into distinct modules: identifying digital magazines associated with users, extracting content items from these magazines, analyzing temporal relationships between associations, and generating recommendations. This segmentation allows the system to handle multiple digital magazines and their complex relationships through manageable, independent processing steps, reducing overall system complexity while maintaining high recommendation relevance
Solution Approach 2:
The patent introduces intermediate data structures and processing layers that mediate between the raw data from multiple digital magazines and the final recommendation output. These intermediaries include user-magazine association records, content item metadata, and temporal relationship indicators, which organize and structure the complex information in a way that simplifies subsequent analysis and recommendation generation
Data Source
AI summary
A digital magazine server allows its users to create digital magazines by including content items in sections of one or more digital magazines. For various pairs of digital magazines, the digital magazine server determines a score based on a number of content items added to a digital magazine that were previously added to an additional digital magazine in a pair. The score indicates a frequency that the additional digital magazine added content items before the digital magazine. Digital magazines may be ranked for a user based on the scores, with the ranking used to recommend digital magazines or other users to the user. Further, the scores and connections between digital magazines may be used to create an influence score for various digital magazines.