Search Result Ranking via Social Context Integration
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Solution Overview
Problem
Current search engines struggle to provide high-quality search results relevant to individual users due to their inability to consider social context and user-specific information, leading to inefficient search experiences, especially on mobile devices with limited screen space.
Innovation Solution
A computer-implemented search tool that accesses user search queries, identifies relevant search results, ranks them based on content and social context, and boosts rankings for features within a social-networking system to increase user interaction, utilizing a graph representation of social-networking information to prioritize search results based on user relationships and behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search engines use generic search algorithms without social context, then search results can be quickly generated, but search result relevance to individual users deteriorates
Solution Approach 1:
The patent introduces a social-networking system as an intermediary that provides social context and user relationship information to the search engine. This intermediary layer enables the search system to access and process social graph data, user interaction history, and relationship information without fundamentally redesigning the core search architecture, thus improving relevance while managing complexity through modular integration.
Solution Approach 2:
The patent changes the parameters used for ranking search results by incorporating social context factors such as user relationships, interaction history, and social network position. Instead of relying solely on traditional search relevance metrics, the system adjusts ranking parameters to weight social signals, thereby improving individualized relevance without requiring a complete overhaul of the search algorithm.
2Measurement precision
If search engines consider social context and user-specific information, then search result relevance improves, but search processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-computing and caching social context information, user profiles, and relationship data before search queries are executed. The system maintains updated social graphs and user interaction histories in advance, so that during actual search processing, this pre-processed information can be quickly retrieved and applied to ranking algorithms, reducing real-time processing requirements while maintaining high relevance.
Solution Approach 2:
The system uses feedback mechanisms to continuously learn from user interactions with search results and social-networking features. By analyzing user behavior patterns and adjusting social context weights dynamically, the system optimizes the balance between processing time and relevance, gradually improving accuracy without requiring excessive computational resources for each query.
3Productivity
If search results are ranked based on social context and user behavior, then user interaction with search results improves, but device resource consumption increases
Solution Approach 1:
The patent applies partial action by selectively processing and applying social context information only to the extent necessary for effective search ranking. Rather than analyzing every possible social signal in full detail, the system identifies and processes the most impactful social factors (such as direct relationships and recent interactions) while filtering out less relevant data, thereby achieving improved user interaction with reduced computational overhead.
Data Source
AI summary
In one embodiment, a computing device may access a search query provided by a user; identify a set of search results in response to the search query, wherein one or more search results in the set are associated with a feature of a social-networking system; rank the set of search results based on one or more factors; boost one or more ranks of the one or more search results associated with the feature to bring the feature to the user's attention; and present the set of search results to the user in order of its ranking.