Content Similarity Filtering for Social Media Feed Redundancy
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Solution Overview
Problem
Information handling devices struggle to effectively filter out similar content items, such as repeated images, news stories, or social media posts, which can clutter user accounts and reduce user engagement.
Innovation Solution
A processor-based system that compares content items to previous items in an account, determines similarity based on matching criteria, and filters out similar content items based on user settings, storing them in a filtered list and optionally displaying indicators for user confirmation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If content items are displayed without filtering, then quantity of content is increased, but content redundancy increases and user engagement decreases
Solution Approach 1:
The patent extracts similar content items from the feed by comparing each content item against previously displayed items using image recognition and metadata analysis. When similarity exceeds a threshold, the similar item is removed from the feed, keeping only unique content while maintaining overall quantity through selective filtering.
2Loss of information
If content filtering is implemented, then content redundancy is reduced, but device complexity increases
Solution Approach 1:
The filtering system is segmented into distinct functional modules: image recognition module for visual content analysis, metadata analysis module for text and context processing, similarity comparison module for matching content, and feed generation module for assembling the final output. This segmentation reduces overall complexity by making each component specialized and manageable.
Solution Approach 2:
Content items are pre-processed and stored with extracted features (image embeddings, metadata tags, text summaries) before being added to the feed. This preliminary action enables faster similarity comparison during feed generation, reducing the computational complexity required at display time.
3Ease of operation
If similar content items are filtered out, then user satisfaction is improved, but processing time increases
Solution Approach 1:
Content items undergo pre-processing where images are converted to feature vectors, text is summarized, and metadata is extracted before being stored in the content database. This preliminary action reduces the processing time required during feed generation, as similarity comparison can be performed efficiently on pre-extracted features rather than raw content.
Solution Approach 2:
The system changes the parameter representation of content from raw images and text to compressed feature vectors and embeddings. This parameter transformation enables faster similarity comparison operations while maintaining accuracy, thereby reducing processing time without compromising user satisfaction.
Data Source
AI summary
Apparatuses, methods, and program products are disclosed for filtering similar content items. One apparatus includes a processor and a memory that stores code executable by the processor. The code is executable by the processor to compare, by use of the processor, a content item to previous content items corresponding to an account. The code is executable by the processor to determine whether the content item is a similar content item for the account based on the comparison between the content item and the previous content items. The code is executable by the processor to, in response to the content item being a similar content item for the account, determine whether to filter the content item from the account.