Default Search Query Generation on Social Networks
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Solution Overview
Problem
Social networking systems face challenges in providing users with effective search functionalities within complex social graphs, as existing methods often fail to offer personalized and engaging search queries that leverage social-graph attributes and user engagement factors.
Innovation Solution
The system generates structured queries that include references to social-graph elements, using user engagement factors, business intelligence, and social-graph affinity to suggest queries that are likely to interest users, along with dynamic and sponsored queries that reflect updates and trending content, allowing users to share queries and results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system provides traditional search functionalities in social networking, then users can perform basic searches, but the search results lack personalization and user engagement
Solution Approach 1:
The system pre-generates a set of default structured queries for each user based on their social-graph elements before the user performs a search. These queries are ready to be presented to the user, eliminating the need for complex query formulation and immediately providing personalized search options tailored to the user's social connections and interests.
Solution Approach 2:
The system dynamically adjusts search query parameters by incorporating user-specific social-graph attributes such as friends, interests, and engagement history. This transforms generic search queries into personalized ones by changing the parameters based on individual user profiles and their relationships within the social network.
2Productivity
If the system generates personalized structured queries based on social-graph elements, then user engagement increases, but the system complexity increases
Solution Approach 1:
The system segments the complex task of query generation into distinct components: identifying social-graph elements (friends, interests, connections), generating default structured queries for each element type, and combining them into a personalized query set. This modular approach manages complexity by breaking down the overall process into manageable segments that can be handled independently.
Solution Approach 2:
The system automatically generates personalized queries using the user's own social-graph data without requiring manual input or configuration. The user's social connections, interests, and engagement patterns serve as the input data that the system processes autonomously to create tailored search queries, eliminating the need for complex manual setup.
3Loss of information
If the system presents multiple default structured queries to users, then relevant search results are provided, but information overload occurs
Solution Approach 1:
The system generates a comprehensive set of default structured queries covering various social-graph elements (friends, interests, connections, events) to ensure thorough coverage of potential search interests. By providing a slightly excessive number of query options, the system ensures that users have access to all relevant search angles without worrying about missing important queries, while the structured format helps users manage the volume.
Data Source
AI summary
In one embodiment, a method includes receiving, from a client system of a user, an indication of the user accessing a query field at the client device of the user, generating a plurality of structured queries that each comprise references to one or more objects associated with the online social network, calculating a score for each structured query based on one or more user-engagement factors, and sending, to the client system responsive to the indication of the user accessing the query field, one or more suggested structured queries for display to the user, each suggested structured query having a score greater than a threshold score.