Adaptive Search Ranking via Social Graph Integration
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Solution Overview
Problem
Current search systems for online content items, such as games, movies, and music, face challenges in improving accuracy, relevancy, and user experience due to the complexity of processing user search queries, particularly in organizing and displaying search results effectively.
Innovation Solution
The implementation of a system that uses popularity-based ranking and social graph information to provide adaptive search results, where content items are displayed in descending order of relevance, incorporating metadata tags and social engagement data to prioritize items popular among the user's online friends and acquaintances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search systems are used to return search results, then the system is simple to operate, but the search accuracy and relevancy are insufficient
Solution Approach 1:
The patent combines multiple data sources including social graph information, popularity metrics, and user interaction data into a unified search ranking system. This integration of diverse information sources enhances search accuracy by considering both traditional relevance factors and social/popularity signals simultaneously.
Solution Approach 2:
The system pre-processes and stores social graph data, popularity metrics, and user interaction information before search queries are submitted. This preliminary preparation of data structures and indexing enables faster, more accurate search results without adding complexity to the real-time search operation.
2Measurement precision
If search results are organized by popularity and social engagement, then the relevancy improves, but the processing complexity increases
Solution Approach 1:
Popularity metrics and social engagement data are pre-calculated and stored in the system before search queries are processed. User interaction histories, friend connections, and content popularity scores are maintained in advance, allowing the search algorithm to quickly retrieve and weight these factors without performing complex calculations during the actual search operation.
Solution Approach 2:
The search ranking system is divided into separate modular components: a social graph analysis module, a popularity metric calculation module, and a result ranking module. Each component handles specific aspects of the ranking process independently, making the overall complex system more manageable and maintainable while improving relevancy.
3Ease of operation
If adaptive search results are provided based on social graph information, then the user experience improves, but the data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant social graph features and popularity metrics needed for search ranking, rather than processing the entire social graph database. By selecting and extracting only the necessary data elements (such as friend connections to similar users, popularity scores of similar content), the system improves user experience while reducing overall data processing requirements.
Data Source
AI summary
The subject disclosure relates systems and methods for returning accurate and relevant search results in an online platform. In some aspects, a process of the disclosed technology can include steps for displaying a first set of search results, the first set of search results including a first plurality of media content items arranged in an order of descending relevance based on search frequency, receiving a first character input from a user of a user device, the first character input defining a first character in a search query, and updating the first set of search results to a second set of search results based on the first character input, the second set of search results comprising at least one media content item not included in the first set of search results that is available for consumption by the user. Systems and machine-readable media are also provided.