Recommender System Temporal Location Extraction
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Solution Overview
Problem
Conventional activity recommender systems face challenges in providing personalized recommendations without requiring users to input specific preference information, as they struggle to accurately extract and interpret implicit and explicit temporal and location information from user content, leading to non-tailored suggestions.
Innovation Solution
An activity recommender system that analyzes text content to identify activity types, willingness, and associated temporal and location information, using keyword and pattern filters to extract and standardize this information, promoting or demoting activities based on user preferences and temporal relevance, and storing this data in a repository for future recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system extracts implicit and explicit temporal and location information from user content, then personalization accuracy is improved, but information extraction complexity increases
Solution Approach 1:
The information extraction process is divided into separate modules: one for extracting explicit temporal information, another for implicit temporal information, and a third for location information. This segmentation allows each module to specialize in specific extraction tasks, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The patent introduces an intermediary processing layer that converts unstructured user content into structured temporal and location data representations. This intermediary layer acts as a bridge between raw user input and the recommendation engine, standardizing information before further processing.
2Measurement precision
If the system analyzes user content to identify activity types and preferences, then recommendation personalization is improved, but processing time increases
Solution Approach 1:
The system performs preliminary analysis of user content to extract temporal and location information in advance, before generating recommendations. By pre-processing user content to identify activity types, preferences, and contextual information, the system reduces processing time during actual recommendation generation while maintaining high personalization accuracy.
3Adaptability or versatility
If the system stores extracted information in a repository for future recommendations, then long-term personalization is improved, but storage requirements increase
Solution Approach 1:
The system extracts only the essential and relevant features from user content for storage in the repository, such as temporal patterns, location preferences, and activity types, rather than storing complete user content. This selective extraction reduces storage requirements while maintaining the ability to provide long-term personalized recommendations.
Data Source
AI summary
One embodiment of the present invention provides a system that recommends activities. During operation, the system receives a piece of content obtained from text or converted to text from speech. The system then analyzes the received content to identify any activity type, indication of willingness to participate in any type of activities, and at least one piece of temporal information, which can be implicitly and/or explicitly stated in the content, and/or one piece of location information associated with the activity type. The system further recommends one or more activities, venues, and/or services that afford or support activities for a user based on the information extracted from the content.