Route-Based POI Recommendation System Using Preference Scores
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Solution Overview
Problem
Existing recommendation systems for points of interest (POIs) often fail to account for individual user preferences and may recommend already visited locations, leading to inaccurate and irrelevant suggestions.
Innovation Solution
A computer-implemented method and electronic device that captures location data to identify user routes and POIs visited, generates user preference scores based on visit frequency and distance, and predicts preferences for unvisited POIs using similar users' scores, recommending new POIs based on proximity and predicted scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If typical recommendation systems use broad advertising approaches, then coverage of recommended POIs is extensive, but relevance to individual user preferences deteriorates
Solution Approach 1:
The system transforms static demographic information into dynamic preference parameters by continuously analyzing location data, visit frequency, and travel distances. This allows the recommendation parameters to adapt and evolve based on actual user behavior patterns rather than relying on fixed demographic categories.
Solution Approach 2:
The system automatically collects and analyzes location data to generate preference profiles without requiring explicit user input about their preferences. Users effectively serve themselves by providing location data that the system then processes to create personalized recommendations, eliminating the need for manual preference surveys.
2Adaptability or versatility
If recommendation systems suggest new POIs outside user's regular routes, then discovery of new locations is improved, but convenience and user acceptance deteriorate
Solution Approach 1:
The system recommends POIs that are partially within or near the user's existing route patterns rather than completely new locations. By suggesting POIs along or near familiar routes, the system achieves a balance between introducing new discovery opportunities and maintaining the convenience of established travel patterns.
Solution Approach 2:
The system pre-calculates preference scores and identifies recommended POIs in advance based on analyzed user patterns. This preliminary analysis allows the system to present convenient recommendations that align with the user's existing behavior patterns before the user actually needs to make a decision.
3Device complexity
If recommendation systems rely on demographic information, then implementation complexity is reduced, but recommendation precision deteriorates
Solution Approach 1:
The system replaces manual demographic data collection and analysis with automated location data processing. Instead of relying on users providing demographic information through surveys or forms, the system automatically tracks and analyzes location patterns, substituting a mechanical data collection process with an automated computational approach.
Solution Approach 2:
The system continuously monitors user location data and uses this feedback to refine and update preference profiles over time. By implementing a feedback loop where location information is constantly collected, analyzed, and used to improve recommendations, the system achieves high precision without requiring complex initial setup.
Data Source
AI summary
A point of interest (POI) may be recommended for a mobile device user based on habits and routines of the user. By automatically and periodically capturing and analyzing location data associated with a mobile device of the user, a route traveled by the user and a plurality of POIs visited by the user may be identified. A user preference score may be generated for each POI indicating a user's preference for visiting that POI. The user preference score may be generated based on the number or frequency of visits by the user to each POI and the distance traveled by the user to visit each POI. Based on user preference scores associated with similar users, user preference scores may be predicted for POIs not visited by the user, and a POI not yet visited by the user may be recommended based on the predicted user preference score of the POI.