Contextual Supplemental Content Presentation via Entity Matching
Solution Overview
Problem
Users of network services often become overwhelmed with the vast amount of content available, making it difficult to view relevant information, as existing filtering methods may miss important content and lead to anxiety about missing relevant data.
Innovation Solution
Techniques are developed to detect primary content being viewed and present relevant supplemental content by analyzing entities within the primary content, matching them with entities in subscribed social media data, and categorizing and displaying the matched social media data alongside the primary content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually filter or sort content through explicit actions, then users can control what they view, but users become overwhelmed by the vast amount of content and may miss important information
Solution Approach 1:
The system automatically detects entities in primary content and retrieves related supplemental content without requiring user intervention. The system serves itself by autonomously filtering and presenting relevant content, eliminating the need for users to manually sort through overwhelming amounts of data while ensuring important information is not missed.
Solution Approach 2:
The system pre-retrieves and caches supplemental content related to entities detected in primary content before the user needs it. By performing the content retrieval and relevance matching in advance, the system reduces the time users would otherwise spend searching or filtering content, while maintaining high precision in content relevance.
2Adaptability or versatility
If users subscribe to multiple social media feeds, then users receive diverse information, but users become overwhelmed and anxious about missing relevant data
Solution Approach 1:
The system extracts only the most relevant supplemental content related to entities in the primary content, separating useful information from the overwhelming volume of subscribed feed data. This extraction process maintains information diversity by pulling from multiple sources while eliminating the need for users to manually manage diverse feeds, as the system automatically filters what matters.
Solution Approach 2:
The entity-based relevance matching system acts as an intermediary between the user's multiple social media subscriptions and the content the user is viewing. Instead of requiring users to directly manage diverse feeds, the intermediary automatically matches and presents relevant content from those feeds based on entity detection, simplifying content management while preserving information diversity.
3Difficulty of detecting and measuring
If explicit filtering methods are used to organize content, then users can find specific information, but important content may be missed and user anxiety increases
Solution Approach 1:
The system automatically detects entities in primary content and retrieves related supplemental content without user intervention, eliminating the risks associated with manual filtering. This self-service approach ensures important content is delivered reliably by using entity-based matching that objectively identifies relevant information rather than relying on user judgment, which may overlook important details.
Solution Approach 2:
The system pre-retrieves and caches supplemental content related to entities in primary content before the user needs it. This preliminary action ensures that important content is already available and organized when the user views the primary content, eliminating the risk of missing information and reducing user anxiety about content delivery reliability.
4Quantity of substance
If supplemental content is presented without context, then users can access additional information, but users cannot determine relevance and may overlook important data
Solution Approach 1:
The entity-based matching system serves as an intermediary that connects supplemental content to the primary content through shared entities. This intermediary relationship ensures that supplemental content is presented with contextual relevance, as it is specifically selected based on entity matches with the primary content the user is viewing, preventing users from overlooking important data while maintaining high relevance accuracy.
Data Source
AI summary
Techniques that may be used for detecting a primary content (e.g., a web page) that the user is viewing and presenting one or more pieces of supplemental content (e.g., social media data) together with the primary content. The supplemental content presented to the user together with the primary content may be content that is matched to the primary content and therefore detected to be relevant to the user. Detection of primary content and matching to supplemental content may be carried out based on a comparison of entities related to the primary and supplemental content. In some embodiments, an analysis of the primary content for entities may include ordering entities according to significance in the primary content and selecting top entities for comparison. Also, in some embodiments, multiple pieces of supplemental content may be displayed to a user categorized based on entity.