Social Network Search Result Categorization Using Multi-Axis Scoring
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Solution Overview
Problem
Social networking systems face challenges in efficiently categorizing and presenting search results from complex social graphs, leading to disorganized and irrelevant outputs due to the lack of category-based ranking and scoring mechanisms.
Innovation Solution
The social networking system calculates category-scores for objects based on multiple scoring axes, such as recency, textual similarity, and author quality, and categorizes objects into specific categories like news, celebrity, or how-to, using weighted arithmetic means for scoring, to provide users with relevant and organized search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If social graph data is stored and processed without category-based organization, then the system maintains simplicity in data structure, but the search results become disorganized and irrelevant
Solution Approach 1:
The patent segments the social graph data into multiple categories (e.g., news, celebrity, how-to, entertainment) and applies category-specific scoring axes to each. This segmentation allows the system to organize complex social graph data into manageable, relevant groups while improving search result precision without overwhelming complexity
Solution Approach 2:
The patent applies different scoring criteria (local quality) to different categories of objects. Each category has its own customized scoring axes and weights tailored to its specific requirements, enabling precise and relevant search results for each category while maintaining overall system manageability
2Measurement precision
If multiple scoring axes are calculated for each object, then the categorization precision improves, but the computational time and resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining multiple scoring axes and category templates before actual search operations. These scoring axes (recency, textual similarity, author quality, etc.) are prepared in advance, allowing the system to quickly calculate category-scores during search without excessive computational overhead
Solution Approach 2:
The patent changes parameters by using weighted arithmetic means to aggregate multiple sub-scores into single category-scores. This parameter transformation reduces the complexity of multi-dimensional scoring while maintaining categorization precision, balancing accuracy with computational efficiency
3Productivity
If category-scores are calculated using weighted arithmetic means of multiple sub-scores, then the search result quality improves, but the computational complexity increases
Solution Approach 1:
The patent transforms multiple sub-score parameters into a single category-score parameter using weighted arithmetic means. This parameter aggregation simplifies the scoring mechanism while maintaining high search result quality, as the weighted average consolidates multiple evaluation dimensions into one comprehensive metric per category
Solution Approach 2:
The patent creates a universal scoring framework that can be applied across all categories. The weighted arithmetic mean approach serves multiple categories simultaneously, providing a multi-functional solution that improves search quality without requiring separate complex mechanisms for each category
Data Source
AI summary
In one embodiment, a method includes receiving a query inputted by the user; identifying a set of objects matching the query; calculating, for each identified object, a plurality of category-scores corresponding to a plurality of categories, respectively, wherein each category-score is calculated based on a plurality of sub-scores corresponding to a plurality of scoring axes; categorizing each identified object into a category of the plurality of categories based on the category-scores for the identified object; and sending, to the client system in response to the query, one or more search results corresponding to one or more of the categorized objects for display, each search result referencing the respective categorized object, wherein the one or more categorized objects of the search results comprises objects categorized into one or more selected categories.