The existing Click-Through Rate (CTR) algorithms have their own advantages and are sensitive to hyperparameters. Quickly obtaining a high-performance CTR model for the new task can bring good application effects. However, ordinary users fail to do so due to the lack of domain knowledge. In this paper, we remedy this deficiency by proposing AutoCTR, an efficient meta-learning based Combined Algorithm Selection and Hyperparameter Optimization (CASH) algorithm, to help non-expert users quickly find the best CTR model. In AutoCTR, we introduce the meta-learning technique to make full use of the meta-information w.r.t. CTR to guide for the new CTR task. Specifically, we utilize the meta-information to learn characteristics and representations of CTR algorithms with different settings. We use these meta experiences combined with few evaluation information on the target CTR dataset to efficiently exploring the huge CTR CASH search space for the new task. The CTR model representation method has significant influence on the quality of the learned meta experiences. To further enhance the experiences quality, we also design a Graph Neural Network (GNN) based embedding learning method. This method can link different CTR models through their components, and thus quickly learning higher-quality model representations. Extensive experimental results show that AutoCTR can quickly select suitable CTR models for different CTR tasks. Compared with the existing CASH algorithms, which ignore meta-information or rely on a huge amount of meta-information, AutoCTR is more reasonable and efficient.

The understanding and cognition of time are the basis for large language models to understand the world. Although large language models (LLMs) have demonstrated strong capabilities in multiple reasoning tasks, they still have significant deficiencies in temporal reasoning, mainly due to the diversity of temporal expressions and the lack of temporal logic reasoning capabilities. In this study, we propose a novel Temporal Chain of Thought framework(TempCoT) to improve the performance of LLM in temporal reasoning tasks through a three-stage reasoning strategy. First, TempCoT explicitly extracts time constraints to ensure the accuracy of time references during reasoning. Second, a semantic retrieval mechanism is introduced to dynamically obtain key temporal facts to enhance the integrity and reliability of information. Finally, an explicit temporal logic reasoning module is constructed based on point algebra to improve the consistency and interpretability of reasoning. Experimental results show that TempCoT significantly improves the temporal reasoning performance of five different LLMs and shows stronger robustness on complex temporal tasks.

In recent years, the problem of traffic congestion has become a hot topic. Accurate traffic flow prediction methods have received extensive attention from many researchers all over the world. Although many methods proposed at present have achieved good results in the field of traffic flow prediction, most of them only consider the static characteristic of traffic data, but do not consider the dynamic characteristic of traffic data. The factors that affect traffic flow prediction are changeable, and they will change over time. In response to this dynamic characteristic, the authors propose a model fusion mechanism based on transformer (TransFusion). The authors adopt two basic forecasting models (TCN and LSTM) as the underlying architectures. In view of the performance of different models on the traffic data at different times, the authors design a model fusion mechanism to assign dynamic weights to basic models at different times. Experiments on three datasets have proved that TransFusion has a significant improvement compared with basic models.

Anomaly detection has been extensively implemented in industry. The reality is that an application may have numerous scenarios where anomalies need to be monitored. However, the complete process of anomaly detection will take much time, including data acquisition, data processing, model training, and model deployment. In particular, some simple scenarios do not require building complex anomaly detection models. This results in a waste of resources. To solve these problems, we build an anomaly detection pipeline(ADOps) to modularize each step. For simple anomaly detection scenarios, no programming is required and new anomaly detection tasks can be created by simply modifying the configuration file. In addition, it can also improve the development efficiency of complex anomaly detection models. We show how users create anomaly detection tasks on the anomaly detection pipeline and how engineers use it to develop anomaly detection models.