- Data Processing and Manipulation
- It extracts the elements needed for personalization from massive data in a particular field and finds data that will help making recommendations.
It manages data systematically so that it can be flexibly used as training data when a new form of data comes in.
- Recommendation Model
- We are developing a recommendation system based on the customer’s past behaviors and patterns, current status, and preference patterns, and through this, we can present a list that customers may want at a specific point in time.
We develop a collaborative filtering deep learning model that recommends items that similar customers may like. We develop Sequence-aware reminder system that analyzes customers' past behavior patterns to predict future patterns and recommend items based on them.
- Knowledge Graph
- Using knowledge graph, we can recommend items which are not in the training dataset to customers.
