Analyzing texts to benefit Marketing purpose.
Machine learning algorithms and computational infrastructure that can be applied are:
- NLP with Bigdata technologies.
- Deep learning to predict user opinions.
- Visualization and statistics about user views.
- Web / Mobile interface.
Personalized search revisited
Hotel Recommendation System
Note: Dinogo’s dataset will be provided to teams doing this challenge after they have signed a NDA (non-disclosure agreement - which states that the dataset must NOT be used for any purposes other than doing this challenge).
Develop a credit scoring model
The model is operated based on a statistical/machine learning system which processes and analyzes borrowers’ credit information in order to distinguish between "good" and "bad" loans and estimate the default probability. By utilizing our substantial source of telecommunications data, we are striving to build a forecasting model that is able to automatically offer each applicant’s credit score which is human-readable and easy-to-interpret.
- Apply voice recognition
- Analyze natural language
- Understand the request and able to response to user's queries
- Able to execute actions following user's request
- Artificial Intelligence can learn from its interactions and become progressively intelligent.
Hồ Chí Minh
Personalization engine to deliver WOW buy and sell experience on Chotot marketplace.
Expected deliverables: Demo application based on anonymized data provided by Cho Tot.
Fraud Detection Application.
Suggested Solution: Develop solution to detect fraudulent documents submitted by customers such as National ID, Driver License, Motor Registration Certificate, Family book...Develop solution to raise red flags to potential frauds using mobile data or any other data such as social data.
Expected deliverables: An interface built on Facebook Messenger and Zalo where customers can apply for loans/credit cards or perform loan/credit card requests
Create a Dynamic Pricing tool (web application).
Build recommendation app for businesses on MoMo (MShop).
- Recommend MShop based on user's history and preferences (personalization).
- Offer targeted promotion for users. MoMo will provide anonymized user data including their transactions, and some other information for building the app and the personalization model.
Building an e-commerce product recommendation system.
Note: Sendo's dataset will be provided to teams doing this challenge after they have signed a non-disclosure agreement (which states that the dataset must NOT be used for any purposes other than doing this challenge).