ברכה שפירא

In recent years, transfer learning has been used successfully to improve the predictive performance of collaborative filtering (CF) for sparse data by transferring patterns across domains. In this work, we advance transfer learning (TL) in recommendation systems (RSs), facilitating improvement within a domain rather than across domains. Specifically, we utilize TL for in-domain usage. This reduces the need to obtain information from additional domains, while achieving stronger single domain results than other state-of-the-art CF methods. We present two new algorithms; the first utilizes different event data within the same domain and boosts recommendations of the target event (e.g., the buy event), and the second algorithm transfers patterns from dense subspaces of the dataset to sparse subspaces. Experiments on real-life and publically available datasets reveal that the proposed methods outperform existing state-of-the-art CF methods.