Travel Information Delivery via Activity-Based Scoring
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Solution Overview
Problem
Existing systems fail to effectively provide personalized travel-related information to users based on their activity indications, often requiring explicit search queries and lacking in relevance and timeliness.
Innovation Solution
A method that identifies user activity indications associated with entities, determines a travel-related score for locations based on these indications, and provides tailored travel information when the score meets a threshold, even outside of search query submissions, using a combination of activity types, times, and user attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the system provides travel-related information only in response to explicit search queries, then the system complexity is reduced, but the information relevance and timeliness deteriorates
Solution Approach 1:
The system performs preliminary analysis of user activity indications (search queries, browsing behavior, reservations) to identify potential travel interests before the user explicitly searches for travel information. This allows the system to proactively prepare and deliver relevant travel information when the user shows interest, improving relevance without requiring the user to formulate specific search queries.
Solution Approach 2:
The system automatically monitors and analyzes user activity patterns to self-identify travel-related interests and preferences without requiring explicit user input or search queries. The system serves itself by generating travel information recommendations based on observed user behavior, reducing the need for complex query processing while maintaining high information relevance.
2Loss of time
If the system continuously monitors user activities to provide timely travel information, then the information timeliness is improved, but the energy consumption increases
Solution Approach 1:
The system periodically analyzes user activity indications rather than continuously monitoring all user actions in real-time. It processes batches of activity data at intervals, identifying travel-related patterns when sufficient data has accumulated. This periodic approach maintains information timeliness by delivering updates when relevant patterns are detected, while significantly reducing energy consumption compared to continuous real-time monitoring.
Solution Approach 2:
The system applies different monitoring intensities to different types of user activities. It closely monitors specific travel-related indicators (flight searches, hotel bookings, destination browsing) while using lighter monitoring for general browsing. This localized quality approach ensures timely detection of travel interests in critical areas while conserving energy in less critical monitoring domains.
3Measurement precision
If the system analyzes multiple activity indications to determine travel interest, then the measurement precision of user intent is improved, but the processing time increases
Solution Approach 1:
The system pre-processes and stores user activity indications as they occur, organizing them by type and destination. When analyzing travel interest, it retrieves pre-organized activity data rather than processing raw logs in real-time. This preliminary organization enables precise multi-factor analysis of user intent while minimizing processing time during the actual travel interest determination phase.
Solution Approach 2:
The system analyzes a focused set of the most relevant activity indications rather than processing all possible user activities. It prioritizes high-weight indicators (explicit travel searches, booking actions) over lower-weight indicators (general browsing), achieving sufficient precision in user intent measurement with a subset of activities, thereby reducing processing time while maintaining accurate intent detection.
Data Source
AI summary
Methods and apparatus for providing travel-related information for a location to a user based on activity indications of the user that are related to the location. The location may be determined based on a set of one or more related activity indications and a travel-related score may be determined for the location that is indicative of likelihood that the user has interest in travelling to the location. The user may be provided the travel-related information for the location based on the travel-related score.