I graduated from Edinburgh University in 2017 with a BSc in Mathematics. Through the introductory courses to Operational Research at my University, I was intrigued by the STOR-i internship programme and took part during summer 2016. I enjoyed my experience so much that I decided to return full time the following year.

My PhD project considers incorporating the Mallows Model into a Recommender System situation.  Recommender systems are machine learning algorithms that make recommendations, by selecting a specific range of items for each individual, which they are most likely to be interested in. The challenge recommender systems face is to sort through a large database and select a small subset of items, which are considered to be the most attractive to each user depending on the context.

Recently a Bayesian inference approach has been developed for the Mallows model, a probability distribution that models ranking data. The framework currently uses MCMC methods to learn and sample from the posterior distribution of the model. However, MCMC is computationally costly if the data arrives sequentially. We are interested to see if we can use Sequential Monte Carlo (SMC) methods to update our inference with the Bayesian Mallows model each time we receive new ranking data.

My project is in collaboration with one of STOR-i’s academic partners, Oslo University. My supervisors are David Leslie (Lancaster) and Arnoldo Frigessi (Oslo).

Homepage: http://www.lancs.ac.uk/~steina1/