34 citations · 101 across the 22 of their papers we have counts for
5 papers · 1 filter
Attack Detection Using Item Vector Shift in Matrix Factorisation Recommenders
Sulthana Shams, Douglas Leith
This paper proposes a novel method for detecting shilling attacks in Matrix Factorization (MF)-based Recommender Systems (RS), in which attackers use false user-item feedback to pr…
High Accuracy and Low Regret for User-Cold-Start Using Latent Bandits
David Young, Douglas Leith
We develop a novel latent-bandit algorithm for tackling the cold-start problem for new users joining a recommender system. This new algorithm significantly outperforms the state of…
Evaluating Impact of User-Cluster Targeted Attacks in Matrix Factorisation Recommenders
Sulthana Shams, Douglas Leith
In practice, users of a Recommender System (RS) fall into a few clusters based on their preferences. In this work, we conduct a systematic study on user-cluster targeted data poiso…
Android OS Privacy Under the Loupe -- A Tale from the East
Haoyu Liu, Douglas J. Leith, Paul Patras
China is currently the country with the largest number of Android smartphone users. We use a combination of static and dynamic code analysis techniques to study the data transmitte…
Bandit Convex Optimisation Revisited: FTRL Achieves Regret
David Young, Douglas Leith, George Iosifidis
We show that a kernel estimator using multiple function evaluations can be easily converted into a sampling-based bandit estimator with expectation equal to the original kernel est…