Travel Content Recommendation by Geographic and User Context
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Solution Overview
Problem
Conventional travel systems fail to utilize relevant user information effectively, resulting in the display of less-relevant content and a sub-optimal user experience.
Innovation Solution
A travel system that receives user location and context characteristics to score and rank content categories and objects, determining their relevance based on user interests, and updates the display interface to prioritize the most relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional travel systems display content without utilizing relevant user information, then the system complexity is reduced, but the content relevance to users deteriorates
Solution Approach 1:
The system pre-collects and stores user information including location data, context characteristics, and content interaction history before content recommendation is needed. This preliminary data gathering enables the system to provide personalized content without adding complexity during the actual recommendation process
Solution Approach 2:
The system introduces a content recommendation engine as an intermediary component that processes user information and matches it with appropriate content. This mediator handles the complex analysis of user preferences and content relevance, isolating the complexity from the core travel system
2Adaptability or versatility
If the system collects and processes user location and context information, then content personalization is improved, but the information processing complexity increases
Solution Approach 1:
The system divides user information into distinct segments: location data, context characteristics (time, weather, activity), and content preferences. Each segment is processed independently by specialized modules, reducing overall processing complexity while maintaining comprehensive personalization
Solution Approach 2:
The system transforms raw user data into standardized parameters and features that can be efficiently processed. User location and context characteristics are converted into structured parameters that facilitate comparison and matching with content attributes
3Loss of information
If the system displays geographically proximate content based on user location, then content relevance is improved, but the data processing requirements increase
Solution Approach 1:
The system calculates content relevance for only the most promising content items based on user location and context, rather than evaluating all available content. This partial evaluation approach reduces data processing requirements while still identifying the most relevant content recommendations
Data Source
AI summary
A travel system generates and provides content recommendations to a user of the travel system. The travel system identifies content categories that are likely to be of interest to the user of the travel system based on context characteristics of the user such as whether the user is a traveler or a local at a particular geographic location. Additionally, the travel system further identifies content objects (e.g., attractions, activities, events, restaurants, businesses, and the like) for each identified content category that are likely to be of interest to the user based on characteristics of each content object. The identified content categories and content objects are provided as content recommendations for display to a user of the travel system, enabling a user to quickly navigate between content categories and content objects within each content category.