Software Recommendation Engine Using User Similarity Scores

The patent pre-calculates and stores similarity scores between users and typicality scores for applications during offline processing, creating ready-to-use recommendation data. This preliminary action allows the system to quickly provide personalized software recommendations without consuming real-time computing resources, resolving the contradiction between providing functional recommendations and preserving processing resources.

The system extracts and analyzes application lists from multiple users to identify patterns and create typical user profiles. By separating the analysis phase from the recommendation delivery phase, the system can process large amounts of user data offline and only deliver concise recommendations online, reducing the storage and processing burden on individual devices while maintaining adaptability.

The patent introduces a recommendation server as an intermediary between users and the software optimization process. This server performs the complex tasks of collecting user data, calculating similarity scores, analyzing application patterns, and generating recommendations. Individual user devices only need to interact with this intermediary to receive simplified recommendations, thereby achieving software optimization without increasing local system complexity.

The system creates simplified copies of user application lists and stores them in a database with associated similarity scores. Instead of requiring complex real-time analysis on each device, the system pre-processes user data into standardized formats and stores these copies for quick retrieval and comparison, reducing computational complexity while maintaining personalization capabilities.

The patent performs data processing in advance by calculating similarity scores between users and computing typicality scores for applications during offline batch processing. User application lists are analyzed, compared, and stored with pre-computed scores before recommendation requests arrive. This preliminary action ensures high recommendation accuracy when users query the system while avoiding real-time processing delays.

The system implements periodic batch processing of user data to update recommendation databases. Instead of continuously analyzing user data in real-time, the system periodically processes accumulated user application lists, recalculates similarity and typicality scores, and updates the recommendation database at scheduled intervals. This periodic action maintains data freshness and accuracy while minimizing continuous processing overhead.