Content Similarity Detection Using Vector Grouping and LSH
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Solution Overview
Problem
Existing content delivery systems face significant challenges in efficiently detecting similar content items across millions of content items, requiring substantial time and computing resources, and struggle to identify variations in content that are not exact duplicates.
Innovation Solution
A system generates numeric vectors for each content item, grouping similar items based on these vectors, and uses hashing techniques like locality-sensitive hashing (LSH) to quickly assign them to buckets, allowing for real-time or near-real-time similarity detection and fraud prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If each content item is compared with every other content item to detect copies, then detection accuracy is improved, but computing resources and time consumption increase significantly
Solution Approach 1:
The patent segments the large-scale content comparison problem into smaller sub-problems by dividing content items into groups based on shared characteristics (e.g., text keywords, image features). Instead of comparing all content items with each other, the system only compares items within the same group, dramatically reducing the number of comparisons needed while maintaining detection accuracy.
2Measurement precision
If each content item is compared with every other content item to detect copies, then detection accuracy is improved, but time consumption increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-processing content items to extract key features (text keywords, image characteristics) and organize them into groups before the actual similarity detection. This pre-organization allows the system to quickly identify which content items warrant detailed comparison, eliminating the need for exhaustive pairwise comparisons and significantly reducing detection time.
3Measurement precision
If strict text and image comparison is performed, then exact duplicate detection is improved, but detection of similar content items deteriorates
Solution Approach 1:
The patent applies local quality by using different comparison strategies for different aspects of content detection. For exact duplicate detection, it uses strict text and image comparison. For detecting similar content items, it employs more flexible similarity metrics that account for variations in text and visual characteristics, allowing the system to adapt its detection approach to the specific detection goal.
Data Source
AI summary
Techniques for efficiently detecting similarity among electronic content items are provided. A vector is generated for each of multiple content items and is used to assign its corresponding content item to a group among multiple groups. A set of content items that is assigned to a particular group of the plurality of groups is identified. For each pair of content items in the set, a similarity score is generated and used to determine whether to classify the pair as similar to each other. Similarity data is updated if to identify the pair if it is determined to classify the pair of content items as similar to each other. The similarity data associates a first content item with a second content item. The second content item is prevented from being presented to a particular user for a period of time after the first content item is presented to the particular user.