Vector Space Model for Menu Item Classification Accuracy
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Solution Overview
Problem
Existing food-ordering services face challenges in accurately determining the geographic locations where restaurant items are delivered and analyzing customer preferences for diverse cuisine consumption, due to limitations in processing and tagging menu items using traditional methods like string matching and unsupervised machine learning.
Innovation Solution
The implementation of a vector space model that converts menu items and tags into numeric vectors, allowing for similarity comparisons using algebraic methods like cosine similarity and Euclidean distance, to automatically classify menu items and determine their appropriate cuisine types and dietary restrictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional string matching and unsupervised machine learning methods are used to tag menu items, then the processing approach is simple to implement, but the accuracy of determining geographic locations and customer preferences is insufficient
Solution Approach 1:
The patent replaces traditional string matching and unsupervised machine learning methods with a vector space model that uses algebraic methods (cosine similarity, Euclidean distance) to classify menu items. This substitution transforms the mechanical text processing approach into a mathematical vector-based system, significantly improving classification accuracy while maintaining computational efficiency.
Solution Approach 2:
The patent transforms menu items from text-based representations to numeric vector representations, changing the parameter space from string matching to continuous numerical values. This parameter transformation enables the use of algebraic methods for similarity comparison, thereby improving the precision of menu item classification and customer preference analysis.
2Ease of operation
If food-ordering services process transactions on behalf of merchants to simplify the ordering process, then customer convenience is improved, but the merchants remain unaware of delivery locations and customer preferences
Solution Approach 1:
The patent implements a feedback mechanism where the food-ordering service analyzes transaction data (including delivery locations and customer preferences) and provides actionable insights back to merchants. This feedback loop enables merchants to understand their customer base and optimize their delivery zones while the service continues to handle transactions, thus resolving the information asymmetry without compromising operational simplicity.
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes transaction data between the food-ordering service and merchants. This intermediary component extracts geographic and preference information from transactions, transforming raw data into useful insights that are communicated to merchants, thereby preventing information loss while maintaining the simplified ordering process.
3Measurement precision
If vector space model is implemented to analyze customer diversity scores and preferences, then recommendation accuracy is improved, but data processing time and resources increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing menu item vectors in a vector space model before actual customer ordering occurs. This preprocessing step transforms raw menu data into structured vector representations that can be quickly compared using algebraic methods, significantly reducing processing time during actual customer transactions while maintaining high analysis accuracy.
Data Source
AI summary
Methods and systems for generating a recommendation to alter the menu of a restaurant based on the transaction data associated with multiple merchants are described herein. In an example, a method generates one or more first representations of one or more items in the menu and comparing the one or more first representations with one or more candidate tags corresponding to generic names associated with menu items. Based on the similarity between the one or more first representations and the one or more candidate tags, the method identifies a portion of the one or more candidate tags. The method further determines that a first characteristic associated with the merchant corresponds to a second characteristic associated with multiple merchants and selects the multiple merchants based on the second characteristic. Based on transaction data associated with the multiple merchants, the method further generates a recommendation to alter the menu.