ML Interaction Party Recommendation for Local Preference Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges in finding interaction parties that resemble their regular interaction patterns efficiently, as they often need to invest substantial time and computing resources to research multiple options, with limited information on the similarity of reviews and preferences.
Innovation Solution
A recommendation system that utilizes machine learning to analyze user interaction history and the history of other users, determining recommended interaction parties based on factors like geographic location, time, and commonalities with past interactions, and provides local entities that match user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually research multiple interaction parties to find similar options, then they can find interaction parties that match their preferences, but they invest substantial time and computing resources
Solution Approach 1:
The system performs preliminary analysis of user interaction history and preferences before the user needs to find new interaction parties. By pre-processing historical data and establishing user profiles in advance, the system prepares recommendation models that can quickly suggest similar interaction parties without requiring users to manually research options at the moment of need.
Solution Approach 2:
The patent introduces a recommendation system as an intermediary between users and interaction parties. This intermediary automatically analyzes interaction patterns, computes similarity metrics, and generates recommendations, eliminating the need for users to directly compare and research multiple interaction parties themselves.
2Measurement precision
If users manually research multiple interaction parties to find similar options, then they can find interaction parties that match their preferences, but they consume substantial computing resources
Solution Approach 1:
The system performs computationally intensive analysis of interaction histories and similarity computations in advance, before users need recommendations. By pre-processing data and establishing user profiles beforehand, the system reduces the computing resources required at the moment of recommendation generation.
Solution Approach 2:
The recommendation system acts as an intermediary that absorbs the computational burden of analyzing interaction patterns and computing similarities. This intermediary performs the resource-intensive processing centrally rather than requiring users' devices to consume substantial computing resources for manual research.
3Reliability
If users have limited information on similarity of reviews and preferences, then they can protect user privacy, but they cannot accurately find interaction parties that match their preferences
Solution Approach 1:
The system extracts only the necessary preference information from user interactions that is needed for generating recommendations, while leaving sensitive personal data on the user's device. By selectively extracting only the minimal required information for similarity computation, the system maintains accuracy while protecting privacy.
Solution Approach 2:
The recommendation system serves as an intermediary that processes preference information in a privacy-preserving manner. It enables accurate matching by computing similarities based on extracted interaction patterns while maintaining appropriate privacy protections through its intermediary role between the user and the recommendation generation process.
Data Source
AI summary
In some implementations, a system may receive interaction data associated with interactions between a user and subsets of a plurality of interaction parties. The system may store the interaction data and the historical interaction data associated with historical interactions of the user. The system may provide the historical interaction data as input to a machine learning model, which may be trained using supervised learning and the historical interactions of the user or historical interactions of one or more other users with one or more of the plurality of interaction parties. The system may receive an output, based on applying the machine learning model to the historical interaction data, that may indicate one or more recommended interaction parties based at least in part on one or more factors, wherein the one or more recommended parties may be local entities local to a geographic location associated with the user.