Search Query Auto-Completion Using Social Graph Ranking
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Solution Overview
Problem
Internet search engines often provide irrelevant search results due to incomplete or poorly formulated search queries, as users may struggle to determine the appropriate terms to include in their queries, leading to less relevant search results.
Innovation Solution
The implementation of a system that uses social graph data to provide users with query auto-completions ranked based on frequency, interaction, endorsement, and selection scores, which are determined using social graph data, to assist users in formulating more relevant search queries by suggesting query auto-completions that are specific to their social relationships and interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional search engines provide query auto-completions based on general usage data, then query suggestions are available to all users, but the relevance of search results deteriorates because they do not account for individual user contexts and social relationships
Solution Approach 1:
The patent segments the query auto-completion system into multiple independent scoring components: frequency score, interaction score, endorsement score, and selection score. Each component evaluates different aspects of query relevance based on social graph data, allowing the system to process complex social relationship information through modular, manageable scoring modules rather than a monolithic complex system
Solution Approach 2:
The patent introduces social graph data as an intermediary layer between user queries and search results. This social graph acts as a mediator that captures user relationships, interactions, and endorsements, transforming complex social context into quantifiable scores that improve query ranking without requiring direct analysis of raw social interaction data
2Measurement precision
If search engines use social graph data to rank query auto-completions, then query relevance improves for individual users, but data processing requirements and system complexity increase
Solution Approach 1:
The patent divides the complex task of query ranking into four distinct scoring mechanisms: frequency score (how often the query is used), interaction score (user interactions with search results), endorsement score (social endorsements from connections), and selection score (user selections from auto-completion lists). This segmentation allows precise measurement of different relevance aspects while keeping each scoring component independently manageable
Solution Approach 2:
The patent transforms qualitative social graph data (relationships, interactions, endorsements) into quantitative parameters that can be processed and compared. By converting social context into numerical scores with specific weightings, the system achieves precise query ranking without requiring complex qualitative analysis of social relationships
Data Source
AI summary
In general, aspects of the subject matter described in this specification can be embodied in methods that include the actions of receiving a search query initial input from a user, receiving a plurality of query auto-completions based on the search query initial input, receiving social graph data, the social graph data being specific to the user, for each query auto-completion of the plurality of query auto-completions, determining a ranking score, the ranking score being determined at least partially based on the social graph data, and transmitting instructions to display the plurality of query auto-completions to the user in a rank order that is determined based on ranking scores.