This project is concerned with developing novel statistical and machine learning methodologies in the area of recommendation systems: matching relevant content (items) to users in an online marketplace, where there is a large number of items and the information about each item and user is very limited. Marketplaces are platforms where users buy and sell various types of items. The items can range from low-value ones such as books and clothes to high-value ones such as cars and real estate properties. Sellers can also post non-tangible items such as job openings and services. Many marketplace sellers are non-professional individuals selling used items, therefore marketplaces can be viewed as a special type of e-commerce that involves a very large number of unique items across multiple categories from a very large and fragmented seller group. This thesis will develop new statistical and machine learning methods to solve some of the important open problems for recommender system in marketplaces, building models, inferential and predictive procedures, and computational codes. The goal is to build and test these models in a real world recommender system. The project is a co-operation between multiple participants: Finn.no with the industrial, computational, algorithmical, data management experience; UiO with a strong experience in statistical models for big data and uncertainty quantification; the University of Lancaster with world leading experts in statistical learning, decision making and stochastic game theory.
Project leader: Helge Jenssen
Institution: FINN NO AS