- document
-
Li, Roger Zhe (author), Urbano, Julián (author), Hanjalic, A. (author)Direct optimization of IR metrics has often been adopted as an approach to devise and develop ranking-based recommender systems. Most methods following this approach (e.g. TFMAP, CLiMF, Top-N-Rank) aim at optimizing the same metric being used for evaluation, under the assumption that this will lead to the best performance. A number of studies...conference paper 2021
- document
-
Slokom, M. (author), Hanjalic, A. (author), Larson, M.A. (author)In this paper, we propose a new privacy solution for the data used to train a recommender system, i.e., the user–item matrix. The user–item matrix contains implicit information, which can be inferred using a classifier, leading to potential privacy violations. Our solution, called Personalized Blurring (PerBlur), is a simple, yet effective,...journal article 2021
- document
-
Kim, Jaehun (author), Urbano, Julián (author), Liem, C.C.S. (author), Hanjalic, A. (author)Deep neural networks have frequently been used to directly learn representations useful for a given task from raw input data. In terms of overall performance metrics, machine learning solutions employing deep representations frequently have been reported to greatly outperform those using hand-crafted feature representations. At the same time,...journal article 2019