Digital Magazine Server Content Similarity via Address Vectorization
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Solution Overview
Problem
Conventional methods are inadequate for accurately determining similarity between diverse content items such as textual articles, image data, audio data, and video data in digital magazines, leading to reduced user interaction.
Innovation Solution
A digital magazine server organizes content items by common characteristics, generates vector representations of address information attributes, and uses similarity measures like cosine similarity to identify and present additional content items that match user preferences, incorporating user interaction data and profile information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional keyword frequency comparison methods are used to determine content similarity, then the method is simple to implement, but the accuracy of similarity determination deteriorates when content items include diverse types such as images, audio, video, and text
Solution Approach 1:
The patent introduces address information (URLs, metadata, source identifiers) as an intermediary representation to compare content items. Instead of directly analyzing diverse content types (images, audio, video, text), the system extracts and compares address information attributes from content sources, which serves as a common intermediary representation that can be uniformly processed regardless of the underlying content type, thereby improving similarity determination accuracy while avoiding the complexity of multi-modal content analysis
Solution Approach 2:
The patent transforms the similarity determination approach by changing the parameters being compared from content-specific features (keyword frequencies in text, visual features in images) to universal address information attributes (source domain, URL structure, metadata properties). This parameter transformation enables consistent comparison across diverse content types by focusing on source characteristics rather than content characteristics, resolving the contradiction between accuracy and complexity
2Reliability
If content items are recommended based on diverse content type analysis, then content relevance improves, but the computational resources and processing time increase
Solution Approach 1:
The patent extracts and utilizes only the address information components from content items (URLs, source identifiers, metadata) rather than processing the entire content. This extraction approach isolates the essential identifying features needed for similarity determination, enabling relevant content recommendations to be generated with significantly reduced computational resource consumption by avoiding full content analysis
3Measurement precision
If address information attributes are extracted and weighted for all content items, then similarity measurement accuracy improves, but the processing time and computational complexity increase
Solution Approach 1:
The patent applies local quality by assigning different weights to different address information attributes based on their relevance to content similarity. Instead of uniformly processing all attributes with equal importance, the system identifies and emphasizes key discriminative attributes (such as source domain, content type indicators, or specific metadata fields) while reducing the weight of less relevant attributes, thereby achieving accurate similarity measurement with reduced processing time by focusing computational effort on the most informative features
Data Source
AI summary
A digital magazine identifies content items for recommendation to a user by determining the address information of one or more content items. The address information of a content item identifies a source from which the content item was received and additional information describing the content item. Representations of content items are generated from the address information for each content item. Additionally, representations of content items previously presented to the user are generated from the address information for the previously presented content items. A measure of similarity between a representation of a content item and a representation of a previously presented content item is determined and used to select content items to present to the user.