Activity Recommendation System Using Locality Filtering
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Solution Overview
Problem
Current methods fail to efficiently provide users with information about new activities they are likely to be interested in, leading to irrelevant data transfer and strain on mobile resources, as they primarily rely on user searches or transaction records that do not account for unknown activities.
Innovation Solution
A computer-implemented method that identifies activities by comparing users based on transaction data, calculating localities, and filtering activities by similarity and novelty to present only relevant and novel activities, reducing data transfer and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a user searches for activities on websites, then the user can find information about activities, but the user must already know what activities to search for and receives much irrelevant data
Solution Approach 1:
The system automatically analyzes user transaction records and behavior data to identify and recommend activities without requiring user initiation or search input. The system serves itself by autonomously processing user data to generate personalized activity recommendations, eliminating the need for users to manually search for activities they may not know about.
Solution Approach 2:
The system uses user transaction records and interaction data as feedback to continuously refine and personalize activity recommendations. By analyzing what users actually do (transaction data) rather than what they say they want, the system adapts to user preferences and behaviors, providing increasingly relevant activity information without requiring active user search.
2Reliability
If transaction records are analyzed to generate preferred activities, then activities the user has performed are identified, but activities new to the user are not considered
Solution Approach 1:
The system merges multiple data sources including transaction records, user profiles, and activity databases to comprehensively identify both familiar and new activities. By combining historical transaction data with broader activity information and user preference analysis, the system simultaneously maintains reliability for known activities while discovering new relevant activities the user has not yet performed.
3Quantity of substance
If all activity information is provided to the user, then comprehensive information is available, but mobile resources are strained due to limited bandwidth and processing power
Solution Approach 1:
The system extracts only the most relevant activity information based on user transaction records and preferences, filtering out unnecessary data before transmission to the user's mobile device. By extracting and delivering only personalized, high-value activity recommendations rather than comprehensive activity databases, the system minimizes data transfer volume and mobile resource consumption while maintaining information quality.
4Adaptability or versatility
If website data is crawled to identify activities, then a wide range of activities can be found, but computational difficulty increases and data accuracy decreases
Solution Approach 1:
The system performs preliminary filtering and validation of activity data using user transaction records before presenting activities to the user. By pre-processing and verifying activity information against actual user behavior data, the system reduces the computational burden of processing raw website data while maintaining high accuracy and relevance of recommended activities.
Data Source
AI summary
A method of providing a user with information related to one or more activities is provided. The method comprises identifying one or more activities; calculating a locality for the user for each activity; identifying a subset of the activities based on the respective locality for each activity; and providing information related to at least one of the subset of the activities to the user.