6 accepted papers
Building click-through rate (CTR) and conversion rate (CVR) prediction models for cross-border e-commerce search requires modeling the correlations among multi-domains. Existing multi-domain methods would suffer severely from poor scalability and low efficiency when number of domains increases. To t…
Projection-free online learning has drawn increasing interest due to its efficiency in solving high-dimensional problems with complicated constraints. However, most existing projection-free online methods focus on minimizing the static regret, which unfortunately fails to capture the challenge of ch…
Model-agnostic meta-learning has garnered attention as a promising technique for enhancing few-shot cross-lingual transfer learning in low-resource scenarios. However, little attention was paid to the impact of data selection strategies on this cross-lingual meta-transfer method, particularly the sa…
The interactive recommender systems involve users in the recommendation procedure by receiving timely user feedback to update the recommendation policy. Therefore, they are widely used in real application scenarios. Previous interactive recommendation methods primarily focus on learning users' perso…
A vast number of well-trained deep networks have been released by developers online for plug-and-play use. These networks specialize in different tasks and in many cases, the data and annotations used to train them are not publicly available. In this paper, we study how to reuse such heterogeneous p…
Cross-domain recommendation methods usually transfer knowledge across different domains implicitly, by sharing model parameters or learning parameter mappings in the latent space. Differing from previous studies, this paper focuses on learning explicit mapping between a user's behaviors (i.e. intera…