Location Data Itinerary Generation Using Sequence Aggregation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for generating travel itineraries do not effectively utilize location data from multiple devices to provide personalized and optimized travel recommendations based on actual user trips and preferences.
Innovation Solution
A data processing system that aggregates location data points from multiple devices to identify popular travel sequences and entities, using probabilistic techniques and expectation maximization to rank and select itineraries that match user queries, including temporal and geo-diverse criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to generate travel itineraries, then the generation process is simple, but the personalization and relevance of recommendations are insufficient
Solution Approach 1:
The system pre-processes location data from multiple devices to generate sequences of visited entities and stores them in a database before queries are made. This preliminary action enables fast, personalized itinerary generation when users submit queries, as the system can directly match queries against pre-processed sequences rather than processing raw data in real-time.
Solution Approach 2:
The system creates anonymized copies of location data sequences from multiple devices and stores them in a centralized database. These copied sequences can be efficiently queried and matched against user requests without accessing or processing the original raw location data, improving both personalization and query speed while maintaining system complexity at manageable levels.
2Measurement precision
If location data from multiple devices is aggregated to improve personalization, then the accuracy of recommendations increases, but the data processing complexity and computational requirements increase
Solution Approach 1:
The system segments the itinerary generation process into distinct functional modules: a data processing system that aggregates and anonymizes location data from multiple devices, a database that stores processed sequences, and a matching engine that queries the database using user location data and temporal constraints. This segmentation distributes computational complexity across separate components, enabling accurate personalized recommendations while managing overall system complexity.
Solution Approach 2:
The system introduces an intermediary database layer that stores pre-processed sequences of visited entities. This intermediary structure acts as a buffer between the raw location data from multiple devices and the query processing function, enabling efficient matching and comparison without directly processing all original data sources simultaneously, thus reducing computational complexity while maintaining recommendation accuracy.
3Adaptability or versatility
If the system considers temporal constraints and geo-diverse sequences to improve itinerary quality, then the personalization increases, but the query processing time increases
Solution Approach 1:
The system pre-processes and organizes location data into sequences with associated temporal information and geo-diverse characteristics before queries are made. By performing this preparation work in advance, the matching engine can quickly retrieve and compare pre-organized sequences against user queries with temporal constraints, providing personalized itineraries without excessive processing delays.
Solution Approach 2:
The system applies different processing and matching strategies to different aspects of itinerary generation: temporal constraints are handled through time-based filtering and comparison, while geo-diversity is handled through spatial analysis of entity sequences. This localized approach to different quality dimensions enables efficient processing of each aspect independently, reducing overall query processing time while maintaining comprehensive personalization.
Data Source
AI summary
A data processing system receives location data points from computing devices. The system annotates the location data points with entities and determines a duration each of the computing devices was at corresponding entities. The system aggregates the location data points into a set of sequences based on the duration and the entities and stores the set of sequences in a data record. The system accesses the database record including a set of sequences generated from location data points received from computing devices. The system receives, from a computing device, a request for a location sequence that includes a query. The system identifies an attribute of the computing device. The system identifies a sequence based on the set of sequences using the query and the attribute. The system transmits the sequence for display on a display device.