Context-Aware Search Selector for Social Networking Systems
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Solution Overview
Problem
Social networking systems lack the ability to determine and utilize search context effectively, leading to challenges in locating relevant information, as current search functionalities do not account for the specific context in which searches are performed.
Innovation Solution
The social networking system learns and applies a search context associated with selector components, allowing it to filter and rank search results based on the context of individual selectors, whether learned automatically or set manually, to provide more relevant information to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the social networking system provides general search functionality, then users can search for any objects in the system, but the search results lack relevance to the specific context in which the search is performed
Solution Approach 1:
The patent segments the search functionality by creating context-specific search results for different selectors. Each selector (e.g., menu search, events search, photos search) receives customized search results based on its associated context, rather than providing a single general search results set. This segmentation allows the system to adapt to different search contexts while maintaining overall search versatility.
Solution Approach 2:
The patent introduces an intermediary component that learns and determines the context associated with each selector. This intermediary layer sits between the general search functionality and the user, translating generic search queries into context-specific results by identifying which selector the user is interacting with and applying the appropriate context filters and rankings.
2Measurement precision
If the system filters search results based on selector context, then the relevance of results improves, but the number of available search results decreases
Solution Approach 1:
The patent changes the parameters of search results dynamically based on the selector context. Instead of using fixed filtering criteria, the system adjusts search parameters such as object type preferences, ranking weights, and result priorities according to the learned context of each selector. This allows the system to maintain a sufficient quantity of results while improving their relevance through parameter optimization rather than rigid filtering.
3Measurement precision
If the system manually configures search context for each selector, then the accuracy of context association improves, but the complexity of system configuration increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically learn and determine the context associated with each selector through machine learning algorithms. Instead of requiring manual configuration, the system observes user interactions and search patterns to autonomously identify which contexts are most relevant to each selector, thereby achieving high context association accuracy without increasing configuration complexity.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors user search behavior and selector usage patterns. This feedback loop allows the system to refine its understanding of selector contexts over time, automatically improving context association accuracy through learned patterns rather than manual configuration, thus avoiding the complexity burden while maintaining precision.
Data Source
AI summary
Embodiments improve the ability of users of a social networking system to search for information that is likely to be relevant to them by learning and/or applying a search context associated with selector components used to search for objects of the social networking system. The search context is specific to the use of an individual selector and thus need not be as general as the context of an entire page or set of pages in which selectors can be embedded. The social networking system may learn the context of a selector by monitoring user selections from prior search results performed using the selector.