Crowdsourced Food Search with Pairwise Nutrition Ranking
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Solution Overview
Problem
Existing health and fitness tracking applications face challenges in providing highly relevant search results for foods and beverages due to the cumbersome nature of finding specific consumables, especially with crowdsourced databases that include duplicative and inaccurately labeled nutritional content.
Innovation Solution
A deep multi-modal pairwise ranking model is employed to rank consumable records based on both food names and nutritional information, using machine learning to adapt to user behavior and provide more relevant search results by converting textual and nutritional data into vectors for accurate comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search methods are used in health tracking applications, then the system is simple to operate, but the relevance of search results deteriorates
Solution Approach 1:
The patent introduces an intermediary ranking system that mediates between the user's search query and the database of consumable items. This intermediary component analyzes both the query and item descriptions using natural language processing, then ranks results based on multiple factors including textual similarity, nutritional content, and user preferences. The intermediary layer processes information without requiring users to manually filter through results, thus improving relevance while managing system complexity through automated intelligence.
Solution Approach 2:
The system transforms the search evaluation from simple keyword matching to a multi-parameter ranking approach. Instead of relying solely on textual match metrics, the system incorporates nutritional parameters (calories, macros, micronutrients), semantic similarity scores, and user profile parameters. By changing the evaluation parameters from binary match/no-match to multi-dimensional scoring, the system achieves more precise relevance assessment while distributing complexity across multiple independent scoring modules.
2Quantity of substance
If crowdsourced databases are used, then the quantity of data increases, but the accuracy of nutritional information deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-processing and validating data before it enters the main search index. When new consumable items are added to the crowdsourced database, the system automatically checks for required fields, validates nutritional information formats, and flags potentially inaccurate entries for review. This preliminary quality control allows the database to maintain large size while filtering out obviously erroneous data, preserving reliability at scale.
Solution Approach 2:
The system implements feedback mechanisms that continuously improve data accuracy. Users can report inaccurate nutritional information, and the system tracks which items receive corrections. The ranking algorithm incorporates confidence scores based on data source reliability and correction history. Over time, this feedback loop refines the quality of the crowdsourced database, allowing the system to leverage large quantities of user-submitted data while automatically correcting inaccuracies through collective intelligence and automated validation.
3Measurement precision
If multiple data types are integrated for ranking, then the accuracy of relevance determination improves, but the processing time increases
Solution Approach 1:
The ranking system segments the relevance determination process into independent modules, each handling a specific data type. Separate components process textual descriptions, nutritional content, and user profile information independently, then combine their results. This segmentation allows parallel processing of different data types and enables the system to optimize each component separately. For example, textual similarity can be computed in advance and cached, while nutritional calculations are performed only when needed, reducing overall processing time while maintaining comprehensive multi-type analysis.
Data Source
AI summary
A method and system for providing more relevant search results and recommendation from a food database is disclosed. The method includes receiving a query, a first candidate food, and a second candidate food. The method includes generating vectors based on the query and food names of the first and second candidate foods using at least one embedding function of a machine learning model. The method includes determining nutrition content vectors from the nutritional data of the first and second candidate foods. The method includes generating a nutrition content vector based on the query using another embedding function of the machine learning model. The method includes determining which of the first and second candidate food is more relevant to the query based at least in part on the nutrition content vectors. The method includes providing search results or recommendation based on the determined relevance.