Content Recommendation System for Broadcasters
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Solution Overview
Problem
Broadcasters face difficulty in identifying high-quality content from vast amounts of user-generated content on online platforms, as existing platforms lack cohesive real-time data filtering and quality filtering, making it hard to incorporate relevant content into their broadcasts.
Innovation Solution
An online system generates aggregated representations of social activity by determining affinity measures between content items and objects in a database, using match keys, grammatical structure, and metadata, and integrates this data into media distributed by partner systems, allowing broadcasters to easily select high-quality content for inclusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If broadcasters navigate through large amounts of content manually to identify content for inclusion, then they can find relevant content, but the time and effort required increases significantly
Solution Approach 1:
The patent introduces an intermediary system (content recommendation system) that acts as a mediator between the vast content repository and the broadcaster. This system automatically analyzes content, determines relevance to broadcast topics, and presents curated content options, eliminating the need for broadcasters to manually navigate through large amounts of content while maintaining high identification accuracy
Solution Approach 2:
The system enables self-service by automatically performing content analysis, relevance determination, and recommendation generation without requiring broadcaster intervention. The content is automatically tagged, categorized, and matched to broadcast topics based on metadata and content analysis, allowing broadcasters to simply select from pre-processed recommendations
2Quantity of substance
If existing online platforms provide large amounts of user-generated content, then content variety increases, but the ability to identify high-quality information decreases
Solution Approach 1:
The patent replaces manual content quality assessment (mechanical human review) with automated content analysis systems that use metadata analysis, topic modeling, and relevance algorithms to evaluate and rank content quality at scale, enabling precise identification of high-quality information from large volumes of user-generated content
Solution Approach 2:
The system implements feedback mechanisms where content performance data, user engagement metrics, and relevance outcomes are continuously analyzed to refine content quality assessment algorithms, improving the precision of high-quality content identification over time while handling increasing content volumes
3Speed
If broadcasters incorporate real-time content from online platforms into broadcasts, then content freshness increases, but the complexity of content filtering and quality assurance increases
Solution Approach 1:
The system performs preliminary actions by pre-processing content in real-time, including automatic tagging, metadata extraction, topic classification, and quality assessment before content reaches the broadcaster. This pre-filtering and organization reduces the complexity of final content selection while maintaining real-time freshness
Solution Approach 2:
The content filtering process is segmented into multiple automated stages: initial content ingestion, metadata extraction, topic matching, quality scoring, and recommendation generation. Each segment handles a specific aspect of filtering, reducing overall system complexity while enabling real-time processing of fresh content
Data Source
AI summary
An online system receives content items from one or more content sources. The content items are mapped to objects in a database of the online system based on measures of affinity of the content items for the objects. When a query identifying an object in the database is received by the online system, the online system identifies content items associated with the identified object. Information describing the identified content items is generated by the online system and provided to a user or entity from which the query was received. Additionally, information describing social engagement with the identified object may be determined based on the content items mapped to the identified object and included in the information describing the identified content items.