Social Graph Search Query Segmentation and Ranking
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Solution Overview
Problem
Social networking systems face inefficiencies in generating relevant search results due to the complexity of social graphs and the limitations of existing search query processing methods, which often produce inadequate results and reduce processing efficiency.
Innovation Solution
The implementation of structured queries with inner and outer constraints, query hinting, and the use of inverse operators to improve search efficiency, along with ranking search results based on user intent, allows for more precise and relevant search outcomes by parsing queries to identify specific social-graph elements and their relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing search query processing methods are used, then the system can handle simple search requests, but the search results are inadequate and processing efficiency is reduced due to social graph complexity
Solution Approach 1:
The patent segments complex social graph search queries into multiple vertical-specific sub-queries (e.g., user vertical, photo vertical, event vertical). Each vertical is searched independently using optimized query processing tailored to its specific data structure and relationships, then results are merged. This segmentation allows simple processing for each vertical while maintaining overall search accuracy across the complex social graph.
2Adaptability or versatility
If complex structured queries with multiple constraints are processed using traditional methods, then comprehensive search coverage is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing aggregated social graph data in vertical-specific indexes before search queries are executed. Frequently accessed relationships and attributes are pre-processed and cached, allowing complex constrained queries to be answered by combining pre-computed results rather than traversing the entire social graph in real-time, thus reducing processing time while maintaining query flexibility.
3Quantity of substance
If the social graph is searched without query optimization, then all possible results are retrieved, but the relevant results are difficult to identify among excessive unrelated results
Solution Approach 1:
The patent applies local quality by implementing vertical-specific scoring and ranking functions that are optimized for each type of social graph data. Instead of using a single generic ranking algorithm, each vertical (users, photos, events) has tailored ranking criteria that prioritize locally relevant attributes. When results are merged from multiple verticals, the local quality rankings are preserved and combined, ensuring that relevant results rise to the top while maintaining appropriate result quantities.
Data Source
AI summary
In one embodiment, a method includes receiving, from a client system of a first user, a query comprising one or more n-grams, determining one or more search intents of the query based at least on whether one or more of the n-grams in the query match terms corresponding to a search intent indexed in a pattern-detection model, generating one or more search results based on the query, each search result corresponding to an object of a plurality of objects, and scoring the search results based on one or more of the search intents.