The goal of this competition is to predict e-commerce clicks, cart additions, and orders. You’ll build a multi-objective recommender system based on previous events in a user session.
Your work will help improve the shopping experience for everyone involved. Customers will receive more tailored recommendations while online retailers may increase their sales.
Online shoppers have their pick of millions of products from large retailers. While such variety may be impressive, having so many options to explore can be overwhelming, resulting in shoppers leaving with empty carts. This neither benefits shoppers seeking to make a purchase nor retailers that missed out on sales. This is one reason online retailers rely on recommender systems to guide shoppers to products that best match their interests and motivations. Using data science to enhance retailers’ ability to predict which products each customer actually wants to see, add to their cart, and order at any given moment of their visit in real-time could improve your customer experience the next time you shop online with your favorite retailer.
Current recommender systems consist of various models with different approaches, ranging from simple matrix factorization to a transformer-type deep neural network. However, no single model exists that can simultaneously optimize multiple objectives. In this competition, you’ll build a single entry to predict click-through, add-to-cart, and conversion rates based on previous same-session events.
With more than 10 million products from over 19,000 brands, OTTO is the largest German online shop. OTTO is a member of the Hamburg-based, multi-national Otto Group, which also subsidizes Crate & Barrel (USA) and 3 Suisses (France).
Your work will help online retailers select more relevant items from a vast range to recommend to their customers based on their real-time behavior. Improving recommendations will ensure navigating through seemingly endless options is more effortless and engaging for shoppers.
Awards:-
- 1st Place – $ 15,000
- 2nd Place – $ 10,000
- 3rd Place – $ 5,000
Deadline:- 24-01-2023
