Multi-Modal Itinerary Recommendation System
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Solution Overview
Problem
Ride sharing platforms face challenges in improving user experience and increasing user engagement by effectively recommending itineraries that cater to individual rider preferences and context, as existing systems lack personalized and flexible transportation options.
Innovation Solution
A system that utilizes processors and computer-readable memories to obtain rider context information and location data, generating and ranking itineraries that include multiple transportation modes, using machine learning models to predict rider preferences and prioritize highly suitable options, thereby enhancing user experience and engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple transportation modes and personalized itineraries are provided, then user experience and engagement are improved, but system complexity increases
Solution Approach 1:
The system segments the itinerary recommendation process into distinct modules: context information acquisition, itinerary generation, machine learning-based ranking, and presentation. This modular segmentation allows each component to be developed and optimized independently, managing system complexity while delivering personalized multi-modal transportation recommendations.
Solution Approach 2:
A machine learning model serves as an intermediary between the raw context information and the final itinerary recommendations. This intermediary automatically learns and applies personalized preferences from historical data, reducing the need for complex manual configuration and enabling scalable personalization across multiple users.
2Measurement precision
If machine learning models are used to rank itineraries, then recommendation accuracy improves, but computational resources and time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing context information and pre-training machine learning models on historical rider data before actual itinerary recommendations are needed. This allows the computationally intensive model training to be done in advance, reducing the computational time required during real-time recommendation generation.
Solution Approach 2:
The system applies partial action by using machine learning models selectively - primarily for ranking itineraries after they are generated by rule-based systems. This hybrid approach achieves high recommendation accuracy through ML while avoiding the excessive computational overhead of using ML for every aspect of itinerary generation.
3Measurement precision
If historical data is collected and processed, then personalization accuracy improves, but data privacy concerns and storage requirements increase
Solution Approach 1:
The system extracts only the essential features and patterns from historical rider data that are necessary for personalization, rather than storing and processing all raw data. By extracting key preferences and behaviors, the system achieves high personalization accuracy while minimizing data storage requirements and privacy risks.
Data Source
AI summary
Itineraries may be recommended using rider context information relating to a rider. The rider context information may be obtained from various sources. Location information including an origin and a destination may obtained. A set of itineraries may be generated based on the rider context information. Each itinerary may include at least one mode of transportation to allow the rider to travel from the origin to the destination. Each itinerary in the set of itineraries may be ranked based on the rider context information.