Personalized Menu Recommendation via Social Media Association Weighting
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Solution Overview
Problem
Existing menu recommendation systems often provide too many suggestions that are not tailored to individual user preferences, as they rely on reviews from unfamiliar users, lacking relevance in similarity of preferences.
Innovation Solution
A computer-implemented method and system that uses text analytics and social media network associations to generate targeted menu item recommendations by weighting user preferences and reviews from socially related users, incorporating natural language processing to analyze user requests and assign values to menu items and reviews.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional menu recommendation systems provide many suggestions, then users have more options, but the recommendations are not tailored to individual user preferences and lack relevance
Solution Approach 1:
The patent introduces social media association data as an intermediary element to bridge the gap between menu recommendations and user preferences. By incorporating friendship networks and social relationships, the system uses these social connections as mediators to infer preference similarity, thereby personalizing recommendations without losing relevant preference information
Solution Approach 2:
The system changes the parameter of recommendation relevance by incorporating social association strength as a weighting factor. Instead of treating all reviews equally, the system adjusts the importance of different reviews based on the social relationship parameters (friendship strength, interaction frequency), thereby transforming the recommendation quality parameter
2Measurement precision
If the system incorporates social media associations and text analytics, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The patent applies multi-functionality by using the social media association database for multiple purposes: it serves as both a source of user preference information and as a weighting mechanism for review importance. The text analytics engine also performs multiple functions including sentiment analysis, keyword extraction, and preference inference, thereby reducing the need for separate specialized components
Solution Approach 2:
The system employs self-service mechanisms where the text analytics automatically extract preference information from social media posts and reviews without manual input. The association strength weights are dynamically calculated based on interaction data, eliminating the need for manual configuration of recommendation parameters
Data Source
AI summary
A computer-implemented method, computer program product, and system for generating a targeted menu item recommendation are provided. The targeted menu item recommendation includes receiving a menu item recommendation request, generating search criteria for the menu item recommendation request, retrieving menu information regarding the search criteria, assigning weighted values to the retrieved information based on the text of the menu item information, preferences of the user, and social media association values, and generating the targeted menu item recommendation.