Social Feed Association Request Context Notification
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Solution Overview
Problem
Users in online social networks often receive unsolicited requests to associate without clear context, requiring them to invest time and effort to understand the basis of the request, which can be inefficient.
Innovation Solution
A system and method that analyze the actions and interactions of the requesting user to generate a notification explaining why they want to associate with the second user, including common interests and actions leading up to the request, providing context within the notification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the system provides detailed context about why a user wants to associate with another user, then the receiving user can make informed decisions faster, but the system complexity increases due to analyzing user actions and generating contextual messages
Solution Approach 1:
The system performs preliminary analysis of the requesting user's actions and interactions before generating the association request notification. By pre-analyzing user behavior patterns, common interests, and interaction history, the system prepares contextual information in advance, allowing the receiving user to immediately understand the basis of the request without the system needing to perform complex analysis at the moment of notification.
Solution Approach 2:
The system introduces an intermediary contextual message that mediates between the requesting user's intentions and the receiving user's decision-making process. This message serves as a bridge, translating complex user behavior data into understandable reasons for the association request, such as common interests or previous interactions, thereby reducing the cognitive load on the receiving user.
2Reliability
If the system analyzes user actions and interactions to generate contextual messages, then the quality of association requests improves, but the processing time and computational resources increase
Solution Approach 1:
The system extracts only the most relevant and meaningful signals from the requesting user's action history, such as common interests, recent interactions, or specific content engagements. By selectively extracting key information rather than analyzing all user actions in detail, the system maintains high-quality contextual messages while reducing processing time and computational overhead.
Solution Approach 2:
The system performs partial analysis of user actions by focusing on specific types of interactions that are most indicative of genuine interest, such as viewing profile pages, liking posts, or commenting. Rather than comprehensively analyzing every single user action, the system concentrates on the most significant signals, achieving reliable contextual information with reduced processing effort.
3Loss of information
If the notification includes multiple signals about user interactions, then the information completeness improves, but the notification becomes more complex and harder to read
Solution Approach 1:
The system segments the contextual information into distinct, organized components within the notification, such as separating common interests from interaction history or grouping related actions together. This segmentation allows the receiving user to process information in manageable chunks, maintaining completeness while improving readability and ease of understanding.
Solution Approach 2:
The system applies local quality by providing different levels of detail for different types of information within the notification. For example, common interests may be highlighted with specific tags or icons, while interaction history is presented as a concise timeline. This differentiated presentation ensures that important information stands out while maintaining overall information completeness without overwhelming the user.
Data Source
AI summary
A system and method is disclosed for determining why a first user has indicated a desire to associate with a second user in an online social network. The first user initiates a request to associate with the second user. When the request is received at the system of the subject technology, one or more signals related to how the first user discovered the second user in the social network are determined, and a message is generated based on the one or more signals. A notification is provided to the second user that includes the message and an indication of the request to convey why the first user indicated a desire to associate with the second user. In some aspects, the subject technology will determine and display, in the notification, common interests that the users share so that the notified user can better evaluate the request.