Crowd-Sourced Venue Recommendations With Incremental AI Feedback
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Solution Overview
Problem
Conventional venue recommendation systems face challenges such as the filter bubble and cold start problem, relying on static databases and failing to adapt to real-time user feedback, leading to inefficient resource use and suboptimal recommendations.
Innovation Solution
A crowd-sourced venue recommendation system using a local software application on mobile devices that collects user feedback to dynamically update a machine learning model, leveraging incremental learning techniques to improve recommendation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional static database systems are used for venue recommendations, then system complexity is low, but recommendation accuracy and adaptability deteriorate due to inability to process real-time user feedback
Solution Approach 1:
The patent implements a dynamic recommendation system that transitions from static database queries to real-time machine learning model inference. The system continuously updates recommendations based on user feedback, location data, and contextual information, making the recommendation process adaptive and dynamic rather than static.
Solution Approach 2:
The system incorporates explicit feedback mechanisms where users rate recommended venues, providing continuous feedback loops. This feedback is processed by the machine learning model to refine future recommendations, creating a self-improving system that increases accuracy over time while managing complexity through iterative learning.
2Measurement precision
If full machine learning model retraining is performed to improve recommendations, then recommendation accuracy improves, but processing time and computational resources increase significantly
Solution Approach 1:
Instead of performing complete model retraining, the system applies incremental learning techniques that update only the necessary portions of the model based on new user feedback. This partial action approach maintains improved accuracy while significantly reducing computational time and resource requirements compared to full retraining.
Solution Approach 2:
The system performs preliminary model training offline before deployment, preparing the base model in advance. During runtime, only lightweight incremental updates are applied based on incoming feedback, eliminating the need for time-consuming full retraining while maintaining high recommendation accuracy.
3Reliability
If comprehensive user feedback collection is implemented, then model training data quality improves, but user experience deteriorates due to excessive prompting and interruptions
Solution Approach 1:
The system implements periodic feedback collection rather than continuous prompting. Users are asked for feedback at strategically determined intervals based on their interaction patterns, ensuring sufficient data collection while preventing excessive interruptions that would degrade user experience.
Solution Approach 2:
The system employs passive feedback collection methods where users can provide feedback autonomously when convenient, and the system infers additional feedback signals from user behavior patterns. This self-service approach maintains data quality while giving users control over their interaction experience.
4Adaptability or versatility
If AI models are deployed on resource-constrained mobile devices, then recommendation personalization improves, but device power consumption and computational load increase
Solution Approach 1:
The system segments the AI processing workload between mobile device and server infrastructure. Lightweight model inference runs locally on the mobile device for immediate personalization, while heavier training and model updates occur on remote servers, distributing computational load and reducing mobile device power consumption.
Solution Approach 2:
The system implements local model caching on mobile devices, storing pre-processed model components that can be quickly loaded and executed. This local quality approach enables fast personalization responses while avoiding repeated heavy computational operations that would consume excessive device power.
Data Source
AI summary
The present disclosure relates to a crowd-sourced venue recommendation system and method thereof. The system includes a local software application executing on a mobile terminal (e.g., a smart phone or a tablet) of a user. The system generates a user interface that allows a user to identify variables for selecting a venue, e.g., a restaurant, bar, hotel, pub, nightclub, etc. The system and method of the present disclosure then recommends a venue to the user based on the selected preferences. The system and method then enables the user to provide feedback in relation to a selected venue to feed an AI model to increase the accuracy of the recommendations based on the user selected preferences. The retraining of the AI model of the system utilizes feedback data provided by the user, crowdsourced training feedback data and/or data from various Internet sources which enables rapid data gathering.