Social Network Profile Card Relevance Scoring
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Solution Overview
Problem
Social networks face challenges in effectively presenting personalized profile cards to members, as existing systems often lack relevance and engagement, failing to highlight viewer information that is pertinent to the member's interests and interactions.
Innovation Solution
A social network system generates enhanced profile cards using a relevance score, which compares viewer and member data to select and highlight relevant information such as job postings, connections, and promotions, thereby personalizing the profile card and increasing member engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If generic profile cards are used to display viewer information, then the system is simple to implement, but the information lacks relevance to member interests and interactions
Solution Approach 1:
The profile card system applies local quality by customizing different sections of the profile card based on relevance scores. Specifically, certain viewer attributes (such as job title, location, or interests) are highlighted or emphasized when they have high relevance to the member's profile, while less relevant attributes are downplayed or omitted. This creates a locally optimized information presentation that adapts to the specific member-viewer relationship.
Solution Approach 2:
The system dynamically changes parameters of the profile card display based on calculated relevance scores. The relevance score itself is a parameter that determines which viewer attributes are displayed, their prominence, and the overall composition of the profile card. By adjusting these display parameters based on the relevance calculation, the system presents information that is optimally tailored to each member's interests and interaction history.
2Productivity
If personalized profile cards with relevant highlights are generated, then member engagement increases, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing relevance scores or relevance metrics for viewer attributes before they are needed for profile card generation. This could involve pre-processing viewer and member profile data to identify potential relevance relationships in advance, so that when a profile card needs to be generated, the system can quickly retrieve and apply pre-computed relevance information rather than performing full relevance analysis at card generation time.
Solution Approach 2:
The system applies partial action by selectively processing and displaying only the most relevant viewer attributes rather than analyzing or displaying all available attributes. The profile card highlights a subset of viewer information that exceeds basic relevance thresholds, providing enhanced personalization without the full computational cost of processing every possible attribute combination.
3Loss of information
If all viewer profile attributes are displayed, then complete information is provided, but the member's attention is diluted and key relevant information is lost
Solution Approach 1:
The system extracts and separates the most relevant viewer attributes from the complete set of profile data. By using relevance scoring, the system identifies and extracts specific attributes (such as common connections, shared interests, or complementary skills) that are most pertinent to the member, presenting these in a highlighted or prominent format while omitting or minimizing less relevant information.
Solution Approach 2:
The profile card is segmented into distinct sections based on relevance levels. Highly relevant attributes are placed in prominent, easily visible sections of the card, while less relevant attributes are either placed in secondary sections or omitted entirely. This segmentation allows the member to quickly identify and focus on the most important information without being overwhelmed by the complete set of viewer attributes.
Data Source
AI summary
Techniques for presenting a profile card personalized to a member of a social network are described. A first viewer profile can be selected, using member data, from a list of profiles of viewers having viewed a first member profile of a first member, the member data having profile attributes associated with each profile. Additionally, a profile card can be generated for the selected first viewer profile based on the profile attributes associated with the first viewer profile. Moreover, a relevance score for a first highlight associated with the selected first viewer profile can be calculated based on the member data of the first member profile. Furthermore, the profile card can be updated to include the first highlight when the calculated relevance score for the first highlight is higher than a predetermined threshold value. Subsequently, a user interface can cause a presentation of the profile card on a display of a device of the first member.