An Active Masked Attention Framework for Many-to-Many Cross-Domain Recommendations

Feng Zhu, Xinxing Yang, Longfei Li, JUN ZHOU

Abstract: Cross-Domain Recommendation (CDR) has been proposed to improve the recommendation accuracy in the target domain (the sparser dataset) by benefiting from the auxiliary information transferred or the knowledge learned from one or many source domains (the denser datasets). However, most of the existing CDR approaches still suffer from the problem of negative transfer caused by undifferentiated knowledge transfer, and thus the recommendation accuracy in some domains, especially in the sparser domains, is still too low, which is not practical in real application scenarios. To address this problem, we propose a novel Active Masked Attention framework, i.e., AMA-CDR, for many-to-many CDR scenarios. Our AMA-CDR pursues a higher goal for CDR approaches, i.e., \textit{improving the recommendation performance in the target domain to achieve a practically usable level}, which is meaningful and challenging in real CDR systems. Specifically, AMA-CDR adopts an end-to-end graph embedding to reduce the objective distortion between graph embedding and embedding combination. More importantly, we propose an active mask for the embedding combination to ease negative transfer, which leverages both the prior knowledge, i.e., data density, and the posterior knowledge, i.e., sample uncertainty. Extensive experiments conducted on two public datasets demonstrate that our proposed AMA-CDR models significantly outperform the state-of-the-art approaches and achieve the new goal.

Primary Subject Area: [Engagement] Multimedia Search and Recommendation

Relevance To Conference: This paper mainly focuses on multi-target cross-domain recommendations, which aim to leverage multiple-source data to help improve the recommendation accuracies in multiple domains. This topic is highly relevant to multimedia/multimodal processing because it includes multiple sub-models in different domains and knowledge transfer learning across multiple domains. The proposed method can be reused in other scenarios of multimedia search and recommendations to improve the search/recommendation performance.

Submission Number: 3985