Recommendation is a basic service to filter information and to guide users from a large pool of items at various online systems, achieving both improved user satisfaction and increased corporate revenues. It works by learning user preferences on items from their historical interactions. Recent deep learning techniques bring in advancements of recommender models with the ability of learning representations of users and items from the interaction data. In real-world scenarios, however, interactions may well be sparse in a target domain of interest, and thus it hurts the huge success of deep models which are depending on large-scale labeled data. Transfer learning is studied to address the data sparsity by transferring the knowledge from auxiliary source domains.

There is a privacy concern...[ Read more ]

Recommendation is a basic service to filter information and to guide users from a large pool of items at various online systems, achieving both improved user satisfaction and increased corporate revenues. It works by learning user preferences on items from their historical interactions. Recent deep learning techniques bring in advancements of recommender models with the ability of learning representations of users and items from the interaction data. In real-world scenarios, however, interactions may well be sparse in a target domain of interest, and thus it hurts the huge success of deep models which are depending on large-scale labeled data. Transfer learning is studied to address the data sparsity by transferring the knowledge from auxiliary source domains.

There is a privacy concern when the source domain shares their data with the target domain. This issue gets worse by the ever-increasing abuses of personal data and it is inevitable due to the enforcement of data protection regulations. Existing research work focuses on improving the recommendation performance while ignores the privacy leakage issue.

In this thesis, we investigate deep knowledge transfer in recommendation, of that the core idea is to answer what to transfer between domains. Specifically, we propose three models in different transfer learning approaches, i.e., deep model-based transfer (DMT), deep instance-based transfer (DIT), and deep feature-based transfer (DFT). Firstly, in DMT, we transfer parameters in lower layers and learn source and target networks in a multi-task way. The CoNet model is introduced to learn dual knowledge transfer across domains and is capable of selecting knowledge to transfer via the sparsity-induced regularization technique enforced on the transfer matrix. Secondly, in DIT, we transfer certain parts of instances in the source domain by adaptively re-weighting them to be used in the target domain. The TransNet model is introduced to learn an adaptive transfer vector to capture relations between the target item and source items. Next, in DFT, we transfer a “good” feature representation that captures the invariant while reduces the difference between domains. The TrNews model is introduced to transfer heterogeneous user interests across domains and transfer item representations selectively. The proposed transfer models can be used for modeling both relational data (e.g., clicks), content data (e.g., news), and their combinations (hybrid data).

Finally, we investigate the adversarial knowledge transfer in recommendation to protect the private attributes in the source domain. Specifically, we propose the PrivNet model which improves the target performance as well as protects the source privacy, of that the core is to learn a privacy-aware neural representation. Through extensive experiments on real-world datasets, we validate the research on adversarial knowledge transfer. This thesis will also describe the research frontier and point out promising future work for investigation.