Personalized Location Recommendation Model Using Collaborative Filtering
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Solution Overview
Problem
Conventional social networking system recommendations often provide inaccurate information due to incomplete or inaccurate data, failing to account for user priorities and being based on opinions from users who are not similar to the recipient, leading to little value and potential misguidance.
Innovation Solution
A system that processes user ratings for various locations to develop a personalized model, incorporating attribute values and collaborative filtering techniques to provide accurate recommendations by correlating user preferences with desired attributes and leveraging ratings from similar users to generate confidence intervals for predicted ratings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommendation systems use general user data and simple algorithms, then the system complexity is low, but the recommendation accuracy and relevance to user preferences deteriorates
Solution Approach 1:
The patent segments the recommendation system into multiple specialized modules: collaborative filtering module for finding similar users, attribute-based module for analyzing location characteristics, confidence interval module for uncertainty quantification, and personalized model development module. Each module handles a specific aspect of the recommendation process, improving accuracy while organizing complexity into manageable components.
Solution Approach 2:
The system performs preliminary actions by pre-calculating user similarities, pre-processing location attributes, and pre-establishing confidence intervals before generating recommendations. This allows the system to quickly retrieve and combine pre-computed information rather than performing complex calculations in real-time, thereby improving recommendation accuracy without proportionally increasing operational complexity.
2Quantity of substance
If the system uses ratings from all users, then more data is available for recommendations, but the relevance to the recipient user's preferences deteriorates because opinions from dissimilar users are included
Solution Approach 1:
The patent applies local quality by transitioning from a uniform approach (using all user ratings equally) to a localized approach where each user receives recommendations based on their specific similarity profile. The system calculates individual similarity metrics between the recipient and other users, then weights ratings accordingly, ensuring that each user gets personalized recommendations reflecting their unique preferences rather than a generic aggregation of all user opinions.
3Reliability
If the system provides recommendations without confidence intervals, then the recommendation process is simpler and faster, but the reliability and trustworthiness of the recommendations deteriorates
Solution Approach 1:
The system implements feedback by calculating confidence intervals for each recommendation and using this information to refine future recommendations. The confidence intervals provide a quantitative measure of recommendation reliability, allowing the system to adjust its modeling approach based on the certainty of predictions. This feedback mechanism enhances recommendation reliability while the modular architecture keeps the increased model complexity manageable through systematic organization of computational tasks.
Data Source
AI summary
Systems, methods, and non-transitory computer readable media are configured to receive ratings for a plurality of locations associated with a location type. The ratings are processed to develop a personalized model for a user to identify candidate locations for the user. At least one candidate location is provided as a recommendation for the user based on the personalized model.