File Recommendation via Keyword Weighting and User History
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Solution Overview
Problem
In cloud computing environments, important files uploaded by users in social networking systems often go unnoticed by others due to lack of relevant recommendations, leading to inefficiencies and missed opportunities for collaboration and information sharing across users with similar interests.
Innovation Solution
A database system processes files to extract keywords and match them with historical keywords of users, generating a recommendation system that suggests relevant files to users based on their interests, using a keyword database that weights keywords by relevance and recent usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If files are uploaded to cloud computing environments, then information is stored and accessible, but users cannot find relevant files leading to missed collaboration opportunities
Solution Approach 1:
The system performs preliminary actions by extracting keywords from files during upload and pre-computing user profiles based on historical interactions before files are needed. This allows the recommendation engine to quickly match files with interested users without real-time processing delays, ensuring timely visibility and collaboration opportunities.
Solution Approach 2:
The system implements feedback mechanisms by analyzing user interactions with recommended files (views, downloads, shares) and using this information to refine future recommendations. User feedback loops continuously improve the accuracy of file recommendations, ensuring that relevant files become visible to the right users based on their evolving interests and behaviors.
2Loss of information
If a recommendation system is implemented to notify users about relevant files, then information sharing improves, but system complexity increases
Solution Approach 1:
The recommendation system is segmented into independent modular components: file processing module that extracts keywords, user profile module that analyzes historical interactions, matching engine that compares files with user interests, and notification module that delivers recommendations. This modular architecture reduces overall system complexity by allowing each component to be developed, maintained, and optimized independently.
Solution Approach 2:
The patent introduces intermediary elements such as keyword databases and user profile databases that act as mediators between files and users. These intermediaries pre-process and structure information, simplifying the matching process and reducing the computational complexity of the core recommendation engine by delegating heavy processing tasks to dedicated intermediary components.
3Measurement precision
If keyword extraction and matching is performed for all files, then recommendation accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary keyword extraction and file processing at the time of upload, storing extracted metadata and keywords in optimized database structures. This preliminary processing eliminates the need for real-time analysis when matching files with users, maintaining high recommendation accuracy while minimizing processing delays during actual file discovery operations.
Data Source
AI summary
Disclosed are examples of systems, apparatus, methods and computer program products for processing a file to generate a recommendation using a database system. A database can be maintained. The database can store data objects identifying historical keywords for a user and weights associated with the historical keywords. Information identifying a file can be received. The file can be processed to extract file keywords. A query attribute can be generated based on the historical keywords, the file keywords, and the weights. It can be determined that the query attribute conforms to a designated characteristic associated with the user. Data indicating a recommendation of the file can be generated in response to determining that the query attribute conforms to the designated characteristic. The data indicating the recommendation can be provided as at least a portion of a feed item of a feed of a social networking system. The feed can be displayed in a user interface on a display device associated with a user of the social networking system.