Crowd-Based Recommendation System Using Multi-Source Data Ranking
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Solution Overview
Problem
Current content discovery platforms face challenges in providing personalized recommendations for items of interest by effectively combining objective and subjective user data, location-based queries, and image analysis to rank business places accurately.
Innovation Solution
A method that uses a recommendation system to obtain and process information about items of interest, incorporating objective data like location and price, subjective user preferences, and image metadata to rank business places based on similarity scores and user behavior, while filtering results based on user-defined criteria and displaying them on a map with routes and additional information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the recommendation system combines multiple data sources (objective information, subjective preferences, image metadata) to improve recommendation accuracy, then the relevance of recommendations is improved, but the system complexity increases
Solution Approach 1:
The system divides the recommendation process into distinct modules: objective information retrieval, subjective preference analysis, image metadata extraction, and ranking function. Each module processes specific data types independently before integrating results, making the complex system manageable and maintainable while achieving high recommendation accuracy through comprehensive data utilization
Solution Approach 2:
The patent introduces a ranking function as an intermediary component that receives and processes multiple data types (objective information, subjective preferences, image metadata) to generate final recommendations. This intermediary layer consolidates complex processing results into a unified recommendation output, simplifying the overall system architecture while maintaining high accuracy
2Measurement precision
If the system processes and integrates multiple data types (location, price, user preferences, image metadata) to provide personalized recommendations, then the relevance of business place listings is improved, but the processing time and computational resources increase
Solution Approach 1:
The system pre-processes and stores objective information, subjective preferences, and image metadata in organized data structures before receiving user queries. This preliminary preparation allows the system to quickly retrieve and process relevant data without performing complex computations in real-time, significantly reducing query processing time while maintaining high recommendation relevance
Solution Approach 2:
The ranking function applies different processing weights and methods to different data types based on their local importance and characteristics. Objectives information, subjective preferences, and image metadata are processed with appropriate specificity to their nature, optimizing processing efficiency while achieving high relevance through localized quality processing rather than uniform treatment
3Measurement precision
If the recommendation system filters and ranks business places based on multiple criteria (location distance, price, user preferences), then the accuracy of personalized recommendations is improved, but the number of processing steps and system complexity increases
Solution Approach 1:
The patent merges multiple filtering and ranking operations into a single integrated ranking function that processes location data, price information, user preferences, and image metadata simultaneously. This consolidation reduces the number of separate processing steps while maintaining high recommendation accuracy through comprehensive multi-criteria evaluation in one unified operation
Data Source
AI summary
Providing a recommendation for a specific item of interest using a recommendation system, by obtaining information concerning items of interest offered by multiple business places, receiving a query concerning the specific item of interest from an electronic device, obtaining objective information concerning business places offering the specific item of interest, and outputting one or more business places that offer the specific item of interest based on a function, said function receiving the objective information as input.