Recent research in time series forecasting has demonstrated significant advancements using models based on the Transformer architecture. However, most existing studies treat each variable as an independent channel, which fails to effectively capture the dynamic dependencies among multiple variables. This limitation impedes the model's ability to understand complex dynamic relationships in real-world data, thereby reducing predictive accuracy. To address this issue, we propose a novel framework called MTS-CAM (Multi-Time-Scale Convolutional Attention Mechanism). MTS-CAM captures various cycles and trends across different time scales and employs a convolutional attention module to identify key features, while dynamically adjusting the importance of each channel and spatial position within the feature map. This comprehensive approach enables the model to account for dynamic relationships and feature variations among variables. Experimental results on six benchmark time series forecasting datasets validate the effectiveness of the proposed model, showing that it significantly outperforms existing methods and achieves state-of-the-art performance.

Relation triplet extraction (RTE) is a fundamental while challenging task in knowledge acquisition, which identifies and extracts all triplets from unstructured text.Despite the recent advancements, the deep integration of the entity-, relation-and triplet-specific information remains a challenge.In this paper, we propose a Graph-based Mixture-of-Experts mutual learning framework for RTE, namely RTE-GMoE, to address this limitation.As a model-agnostic framework, RTE-GMoE distinguishes itself by including and modeling the mutual interactions among three vital taskspecific experts: entity expert, RTE expert, and relation expert.RTE expert corresponds to the main RTE task and can be implemented by any model and the other two correspond to the two auxiliary tasks: entity recognition and relation extraction.We construct an expert graph and achieve comprehensive and adaptive graph-based MoE interactions with a novel mutual learning mechanism.In our framework, these experts perform knowledge extractions collaboratively via dynamic information exchange and knowledge sharing.We conduct extensive experiments on four state-ofthe-art backbones and evaluate them on several widely-used benchmarks.The results demonstrate that our framework brings consistent and promising improvements on all backbones and benchmarks.Component study and model analysis further verify the effectiveness and advantages of our method.

Feature explanation plays an increasingly essential role in the e‐commerce search platform. Most of the existing studies focus on modeling the user’s interests to estimate the click‐through rate (CTR). A good e‐commerce system not only needs precise ranking to inspire users’ shopping desire but also needs feature explanation to meet the demands of shop owners. The e‐commerce traffic health of merchants is very important. How to effectively achieve shop owners’ multiple goals still remains as an open problem. In industrial search systems, merchants’ key demands mainly include two aspects. On the one hand, merchants want to know rule analysis of online traffic distribution, so as to help them understand the logistics of online traffic. On the other hand, they need relevant online traffic participation tools, which instruct them to participate. To address these issues, we propose a factor marginal effect analysis approach (FMEA) based on game theory, which can compute the contribution of one‐dimensional features to the enhancement of online traffic. First, we use machine learning to model the business target. Then, we improve the SHAP value algorithm, which can provide clear business insights. Finally, we calculate the marginal effect of each feature on the business outcome. In this way, we provide a traffic analysis guidance method and address merchants’ participation challenges. In fact, the FMEA has been deployed in a real‐world Large‐Internet‐Company’s App search systems and successfully serves online e‐commerce service to over hundreds of millions of consumers. Our approach can guide operational decisions effectively and bring +10.05% revenue for the flow index, +7.54% for the user feedback index, and +2.46% for the service index.

Traditional Chinese Medicine (TCM) symptoms are the basis of the diagnosis and differentiation. Analyzing TCM symptoms is significant for discovering the knowledge of TCM. Different doctors of TCM prefers using different terms for the same symptom, which is not conducive to the standardization of TCM knowledge and hinders the heritage of TCM. This paper presents a solution to structure the symptoms of TCM by constructing two lists, standard TCM symptom list and synonym list), which take standardization rules of TCM symptoms into account. In addition, the algorithm of Chinese literal similarity with the parameters fine-tuned, is applied in field of TCM. Experimental results have shown the effectiveness of the proposed solution.