17456401. ARTIFICIAL INTELLIGENCE FOR TRAVEL PARTNER AND DESTINATION RECOMMENDATIONS simplified abstract (International Business Machines Corporation)
ARTIFICIAL INTELLIGENCE FOR TRAVEL PARTNER AND DESTINATION RECOMMENDATIONS
Organization Name
International Business Machines Corporation
Inventor(s)
Pinaki Bhattacharya of Pune (IN)
ARTIFICIAL INTELLIGENCE FOR TRAVEL PARTNER AND DESTINATION RECOMMENDATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17456401 titled 'ARTIFICIAL INTELLIGENCE FOR TRAVEL PARTNER AND DESTINATION RECOMMENDATIONS
Simplified Explanation
The patent application describes a method, computer system, and computer program for enhancing travel recommendations. Here are the key points:
- The system takes a user's travel preferences as input and uses a machine learning model to find a matching travel profile from a database.
- The machine learning model searches the database and generates a weighted directed acyclic graph based on the properties of the travel profiles.
- The graph has nodes representing the user's travel profile and the matching travel profile.
- The system generates and transmits messages presenting the match to the user.
Potential Applications
This technology can be applied in various areas related to travel recommendations, such as:
- Online travel agencies: Enhancing the accuracy and relevance of travel recommendations provided to users.
- Travel planning platforms: Helping users find suitable travel companions based on their preferences.
- Social networking platforms: Facilitating connections between users with similar travel preferences.
Problems Solved
This technology addresses several challenges in travel recommendation systems:
- Lack of personalized recommendations: By using machine learning and matching algorithms, the system provides tailored travel recommendations based on individual preferences.
- Inefficient search and matching: The use of a weighted directed acyclic graph improves the efficiency and accuracy of finding matching travel profiles.
- Limited user engagement: By generating and transmitting messages presenting the match, the system increases user engagement and encourages interaction with the recommendations.
Benefits
The use of this technology offers several benefits:
- Improved user satisfaction: By providing personalized travel recommendations, users are more likely to find options that align with their preferences, leading to higher satisfaction.
- Time and effort savings: The system automates the process of finding matching travel profiles, saving users time and effort in searching for suitable travel companions or destinations.
- Enhanced user engagement: The generation and transmission of messages presenting the match increase user engagement and interaction with the travel recommendations.
Original Abstract Submitted
A method, computer system, and a computer program product for travel recommendation enhancement are provided. A first travel profile that includes travel preferences is input into a first machine learning model. In response to the inputting, a second travel profile is received from the first machine learning model as a match for the first travel profile. The first machine learning model searches a database of travel profiles and generates at least one weighted directed acyclic graph based on properties of the travel profiles to determine the match. First and second nodes of the at least one weighted directed acyclic graph correspond to the first travel profile and to the second travel profile, respectively. One or more messages presenting the match are generated and transmitted.
