Cross Domain Deep Collaborative Filtering without Overlapping Data
Meng Liu, Jianjun Li, Guohui Li, Zhiqiang Guo, Chaoyang Wang, Peng Pan · 2023
Cross-domain collaborative filtering (CDCF) is an effective method to alleviate the data sparsity problem by transferring knowledge from a source domain to assist the learning of a target domain. However, most of the existing CDCF approaches require that the two domains have at least one overlapping side (either on user or item) and the raw data can be fully shared across domains, which is difficult to be satisfied in reality due to corporate barriers and the risk of user privacy leakage. Although there are some attempts on applying CDCF to the scenario without overlapping data by transferring cluster-level rating patterns, these methods fail to mine the complex connections between the two domains, which makes their performance still not satisfactory. To address these problems, we propose a novel deep Interaction Distribution Transfer (IDT) framework, which extracts and transfers knowledge from the feature distribution formed by the whole dataset rather than specific data. In this way, the knowledge is embedded into high-order features for transfer, which can effectively avoid privacy leakage during the data sharing process. Moreover, as a flexible framework, IDT obtains powerful feature extraction ability from the base model, which guarantees its superior performance. Extensive experiments on three benchmark datasets are conducted and the results verify the effectiveness of the proposed framework.