Personalization engine to deliver WOW buy and sell experience on Chotot marketplace.

There are hundreds of thousands of new ads posted on Chotot on a daily basis that being viewed by millions of potential buyers everyday. These are items ranging across different categories such as Vehicle, Property, Electronics, Pets and others hobbies and appliances. How to make full use of these fresh contents data to create an application or web feature that increase the personalized user experience in buy and sell such that one person will find joy, fun and good deals everyday from the first day visiting Chotot.

Expected deliverables: Demo application based on anonymized data provided by Cho Tot.

Fraud Detection Application.

FEC is currently offering many products and services over the digital channels and thus face many online frauds from customers such as using fake IDs to apply or using unauthorized information from others to apply for loans online.

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).

Dynamic Pricing: Automate adjust the products's price base on sales volume. Once the sellers have a lot of SKUs, pricing control will be become very difficult, the automatic update pricring base on the pre-configured rules makes it easy for the seller to control the sales of a product, help them easily optimizing profits, and selling more products.

Build recommendation app for businesses on MoMo (MShop).

An MShop is a business that accepts payments with MoMo. It could be anything from a supermarket like Coop Mart, to a convenient store like Circle K, to a restaurant like Mon Hue or Thai Express, to a coffee shop like Phuc Long, to a street food vendor like MotoCoffee. There are more than ten thousand MShops all over Vietnam, cover the majority of a person's daily needs. For users, they usually want to explore a new service or try something new with confidence. However, there is usually too many options to choose from. We want to make it more convenient for users to find something new, based on merchant and MoMo data. A few directions that we can do: • Make it easy for users to search businesses within a location. They could even see some of the reviews about the shop or the business.
  • 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.

On an e-commerce website like, there are millions of customers and millions of products. In order to provide a customer with a great shopping experience, it is important to understand his or her preferences and recommend to him / her products that are most likely of his / her interest. By providing personalized product recommendations, the website will also greatly improve its revenues. Given Sendo's proprietary dataset of historical customer behavior data (e.g., the products that a particular customer has viewed in the past), how would you build an effective product recommendation system using machine learning?

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).