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Publications (6)
Deep learning has achieved significant breakthroughs in medical imaging, but these advancements are often dependent on large, well-annotated datasets. However, obtaining such datasets poses a significant challenge, as it requires time-consuming and labor-intensive annotations from medical experts. Consequently, there is growing interest in learning...
As deep learning (DL) technologies have developed rapidly, many new techniques have become available for recommender systems. Yet, there is very little research addressing how users’ feedback for particular items (such as ratings) can affect recommendations. This feedback can assist in building more fine-grained user profiles, as not all raw clicks...
Despite many recent advances, state-of-the-art recommender systems still struggle to achieve good performance with sparse datasets. To address the sparsity issue, transfer learning techniques have been investigated for recommender systems, but they tend to impose strict constraints on the content and structure of the data in the source and target d...
