Travel Service Search Personalization via Segmentation
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Solution Overview
Problem
Current travel service search tools are cumbersome and fail to provide personalized recommendations that allow users to trade-off between various travel attributes, such as flight time and price, and do not consider user preferences or social connections.
Innovation Solution
A server computer system that classifies travel options into personalized groups based on past transactions, user preferences, domain expert input, semantic analysis, and company policies, allowing users to vote on attribute weights and receive bucketed recommendations tailored to their behavior and social connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If travel options are presented in a comprehensive and detailed manner, then the information completeness is improved, but the complexity of the search process increases and user decision-making becomes more difficult
Solution Approach 1:
The patent segments travel options into distinct buckets based on user preferences and attributes (e.g., cheapest, fastest, most comfortable). This segmentation organizes comprehensive information into manageable groups, reducing search complexity while maintaining information completeness. Users can explore detailed options within each bucket without being overwhelmed by the entire set of travel options.
Solution Approach 2:
The system introduces an intermediary classification layer (buckets) between the raw travel options and the user. This intermediary organizes and pre-processes information according to user preferences, acting as a mediator that simplifies the presentation of comprehensive data without losing important details.
2Adaptability or versatility
If travel recommendations are personalized based on user preferences and behavior, then user satisfaction is improved, but the system complexity increases due to need for data collection and analysis
Solution Approach 1:
The system performs preliminary actions by collecting user preference data and behavior patterns in advance, then uses this pre-processed information to automatically classify travel options into personalized buckets. This preliminary data collection and analysis enables personalization without adding complexity during the actual search process, as the classification logic is pre-configured based on user profiles.
Solution Approach 2:
The system implements feedback mechanisms where user interactions with travel options (clicks, selections, searches) are continuously analyzed and used to refine personalization. This feedback loop allows the system to adapt to user preferences dynamically, improving personalization capability while managing complexity through automated learning rather than manual configuration.
3Ease of operation
If the system provides detailed classification and grouping of travel options, then the ease of operation is improved, but the computational resources and processing time increase
Solution Approach 1:
By segmenting travel options into predefined buckets based on key attributes (price, duration, comfort), the system enables easy user navigation without requiring complex real-time computations. The segmentation is performed once during data processing, and users can quickly filter and compare options within relevant buckets, reducing both operational complexity and processing time during actual searches.
Data Source
AI summary
A system and method for searching travel services. A server computer receives a travel request from a client device operated by a user. The server computer identifies travel options according to the travel request. The server computer classifies the travel options into predefined groups, the classifying based on at least one of past transactions, input from domain experts, input from semantic analysts, analytics data, user preferences, and company policies. The server computer presents the options via presentation of the predefined groups.