Taxonomy completion aims to integrate new concepts into existing taxonomies by determining their appropriate hypernym and hyponym. While semantic and structural information are crucial for this task, existing approaches often struggle to balance these aspects effectively. In this paper, we propose COMI, an efficient taxonomy completion framework that leverages large language models (LLMs) to capture both semantic and structural information in a unified manner. COMI compresses node semantics into token representations, enabling LLMs to efficiently process the input structure composed of these tokens. To enhance the model's understanding of the structure, a further fine-tuning process using contrastive learning with mixup data augmentation is applied, where mixup generates diverse and challenging negative samples. Through these innovations, COMI improves the integration of semantic and structural information, leading to more accurate taxonomy completion. The experimental results on three real-world datasets demonstrate that COMI achieves state-of-the-art performance while showing up to 284x faster inference compared to the previous best method. Our code and compressed tokens are available at https://github.com/cyclexu/COMI.

Session-based recommendation (SR) gains increasing popularity because it helps greatly maintain users' privacy. Aside from its efficacy, explainability is also critical for developing a successful SR model, since it can improve the persuasiveness of the results, the users' satisfaction, and the debugging efficiency. However, the majority of current SR models are unexplainable and even those that claim to be interpretable cannot provide clear and convincing explanations of users' intentions and how they influence the models' decisions. To solve this problem, in this research, we propose a meta-path guided model which uses path instances to capture item dependencies, explicitly reveal the underlying motives, and illustrate the entire reasoning process. To begin with, our model explores meta-path guided instances and leverages the multi-head self-attention mechanism to disclose the hidden motivations beneath these path instances. To comprehensively model the user interest and interest shifting, we search paths in both adjacent and non-adjacent items. Then, we update item representations by incorporating the user-item interactions and meta-path-based context sequentially. Compared with recent strong baselines, our method is competent to the SOTA performance on three datasets and meanwhile provides sound and clear explanations.

Time series classification presents a specific machine learning challenge due to the ordering of variables. Recent studies show that the simple nearest neighbor classifier with elastic distance measures is hard to beat and many researchers focus on alternative distance measures. Unlike nearest neighbor classifier try to find a training sample which has the minimum distance with test instance, we utilize a reconstruction strategy to determine the label of new time series in this paper. Concretely, for each test time series, we reconstruct it by using as few training samples as possible and then calculate the residuals between the test time series and the selected training samples of each class. The test time series is classified to the class with minimum residual. To get the required time series from the training set, we employ sparse restriction technique to discover the optimal combination of different training samples while fitting test time series. Meanwhile, to solve the scenarios where the time series dataset is linearly inseparable, we extend our method by the kernel trick. Extensive experimental results show that the proposed method can gain the significant improvement on commonly used time series datasets.