18507953. MULTI-MODALITY SYSTEM FOR RECOMMENDING MULTIPLE ITEMS USING INTERACTION AND METHOD OF OPERATING THE SAME simplified abstract (ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE)
Contents
- 1 MULTI-MODALITY SYSTEM FOR RECOMMENDING MULTIPLE ITEMS USING INTERACTION AND METHOD OF OPERATING THE SAME
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MULTI-MODALITY SYSTEM FOR RECOMMENDING MULTIPLE ITEMS USING INTERACTION AND METHOD OF OPERATING THE SAME - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
MULTI-MODALITY SYSTEM FOR RECOMMENDING MULTIPLE ITEMS USING INTERACTION AND METHOD OF OPERATING THE SAME
Organization Name
ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE
Inventor(s)
Jeong Min Yang of Daejeon (KR)
Byung Hyun Yoo of Daejeon (KR)
MULTI-MODALITY SYSTEM FOR RECOMMENDING MULTIPLE ITEMS USING INTERACTION AND METHOD OF OPERATING THE SAME - A simplified explanation of the abstract
This abstract first appeared for US patent application 18507953 titled 'MULTI-MODALITY SYSTEM FOR RECOMMENDING MULTIPLE ITEMS USING INTERACTION AND METHOD OF OPERATING THE SAME
Simplified Explanation
The present invention is a multi-modality system for recommending multiple items using an interaction, which includes an interaction data preprocessing module, an item data preprocessing module, and a learning module with a neural network model.
- The multi-modality system preprocesses interaction data and item information data to convert them into training data.
- The learning module uses the training data to train a neural network model, which outputs a set of recommended items based on a conversation context with a user.
Potential Applications
This technology could be applied in e-commerce platforms to provide personalized product recommendations to users based on their interactions.
Problems Solved
This technology solves the problem of efficiently recommending multiple items to users by analyzing their interactions and preferences.
Benefits
The system can enhance user experience by offering tailored recommendations, leading to increased user engagement and potentially higher conversion rates.
Potential Commercial Applications
A potential commercial application of this technology could be in online retail platforms to improve product discovery and increase sales through personalized recommendations.
Possible Prior Art
One possible prior art could be collaborative filtering algorithms used in recommendation systems, which also analyze user interactions to provide personalized recommendations.
What are the limitations of the neural network model used in this system?
The limitations of the neural network model used in this system could include overfitting, scalability issues with large datasets, and the need for extensive computational resources for training.
How does this system handle user privacy and data security concerns?
This system could address user privacy and data security concerns by implementing encryption techniques, anonymizing user data, and complying with relevant data protection regulations such as GDPR.
Original Abstract Submitted
The present invention relates to a multi-modality system for recommending multiple items using an interaction and a method of operating the same. The multi-modality system includes an interaction data preprocessing module that preprocesses an interaction data set and converts the preprocessed interaction data set into interaction training data; an item data preprocessing module that preprocesses item information data and converts the preprocessed item information data into item training data; and a learning module that includes a neural network model that is trained using the interaction training data and the item training data and outputs a result including a set of recommended items using a conversation context with a user as input.
