Machine learning techniques are widely used to address many recommendation scenarios – such as suggesting a movie to watch on (e.g.) Netflix, or recommending a point-of-interest to visit in a city, often by learning from historical user data. However, recommendation systems can be influenced by what users have already been recommended and thereafter viewed/visited, rather than what these systems might have found to be relevant of their own accord – for instance, Netflix might start to recommend movies that are already popular from its previous recommendations.
Such an effect can be described as a filter-bubble or a closed-loop feedback, and has been typically avoided through introducing novel or serendipitous recommendations into the suggestions. However, the alternative use of approaches originating from closed-loop theory, such as intermittent control, have not been systematically investigated within recommender systems.
This PhD will be focussed on applying ideas and techniques from closed-loop theory to state-of-the-art recommender systems. The candidate will investigate the modelling and deployment of closed-loop recommender systems using new neural networks architectures in comparison and along traditional matrix factorization and BPR-based recommenders. The evaluation of the resulting systems will be conducted using both public benchmarks in recommender systems as well as within the experimental pipeline of some of our data partners in the EPSRC Closed-Loop Data Science project.
The successful candidate will have a strong interest/background in recommender systems, machine learning, and/or information retrieval.