When new users join a recommender system, traditional approaches encounter challenges in accurately understanding their interests due to the absence of historical user behavior data, thus making it difficult to provide personalized recommendations. Currently, two main methods are employed to address this issue from different perspectives. One approach is centered on meta-learning, enabling models to adapt faster to new tasks by sharing knowledge and experiences across multiple tasks. However, these methods often overlook potential improvements based on cross-domain information. The other method involves cross-domain recommender systems, which transfer learned knowledge to different domains using shared models and transfer learning techniques. Nonetheless, this approach has certain limitations, as it necessitates a substantial amount of labeled data for training and may not accurately capture users’ latent preferences when dealing with a limited number of samples. Therefore, a crucial need arises to devise a novel method that amalgamates cross-domain information and latent preference extraction to address this challenge. To accomplish this objective, we propose a Cross-domain Recommender System based on Domain Knowledge Transferor and Latent Preference Extractor (TECDR).  In TECDR, we have designed a Latent Preference Extractor that transforms user behaviors into representations of their latent interests in items. Additionally, we have introduced a Domain Knowledge Transfer mechanism for transferring knowledge and patterns between domains. Moreover, we leverage meta-learning-based optimization methods to assist the model in adapting to new tasks. The experimental results from three cross-domain scenarios demonstrate that TECDR exhibits outstanding performance across various cross-domain recommender scenarios.

Despite recent advancements in utilizing meta-learning for addressing the generalization challenges of graph neural networks (GNN), their performance in argumentation mining tasks, such as argument classifications, remains relatively limited. This is primarily due to the under-utilization of potential pattern knowledge intrinsic to argumentation structures. To address this issue, our study proposes a two-stage, pattern-based meta-GNN method in contrast to conventional pattern-free meta-GNN approaches. Initially, our method focuses on learning a high-level pattern representation to effectively capture the pattern knowledge within an argumentation structure and then predicts edge types. It then utilizes a meta-learning framework in the second stage, designed to train a meta-learner based on the predicted edge types. This feature allows for rapid generalization to novel argumentation graphs. Through experiments on real English discussion datasets spanning diverse topics, our results demonstrate that our proposed method substantially outperforms conventional pattern-free GNN approaches, signifying a significant stride forward in this domain.

We extend the framework of cost-sensitive classification to mitigate risks of huge costs occurring with low probabilities, and propose an algorithm that achieves this goal. Instead of minimizing the expected cost commonly used in cost-sensitive learning, our algorithm minimizes conditional value-at-risk, also known as expected shortfall, which is considered a good risk metric in the area of financial engineering. The proposed algorithm is a general meta-learning algorithm that can exploit existing example-dependent cost-sensitive learning algorithms, and is capable of dealing with not only alternative actions in ordinary classification tasks, but also allocative actions in resource-allocation type tasks. Experiments on tasks with example-dependent costs show promising results.