In this study, we focus on the problem of information expiration when using the traditional collaborative filtering algorithm and propose a new collaborative filtering algorithm by integrating the time factor (ITWCF). This algorithm considers information influence attenuation over time, introduces an information retention period based on the information half‐value period, and proposes a time‐weighted function, which is applied to the nearest neighbor selection and score prediction to assign different time weights to the scores. In addition, to further improve the quality of the nearest neighbor selection and alleviate the problem of data sparsity, a method of calculating users’ sentiment tendency by analysis of user review features is proposed to mine users’ attitudes about the reviewed items, which expands the score matrix. The time factor and sentiment tendency are then integrated into the K‐means clustering algorithm to select the nearest neighbor. A hybrid collaborative filtering model (TWCHR) based on the improved K‐means clustering algorithm is then proposed, by combining item‐based and user‐based collaborative filtering. Finally, the experimental results show that the proposed algorithm can address the time effect and sentiment analysis in recommendations and improve the predictive performance of the model.

Large language models (LLMs) have recently demonstrated exceptional capabilities across a variety of linguistic tasks including question answering (QA). However, it remains challenging to assess their performance in astronomical QA due to the lack of comprehensive benchmark datasets. To bridge this gap, we construct Astro-QA, the first benchmark dataset specifically for QA in astronomy. The dataset contains a collection of 3,082 questions of six types in both English and Chinese, along with standard (reference) answers and related material. These questions encompass several core branches of astronomy, including astrophysics, astrometry, celestial mechanics, history of astronomy, and astronomical techniques and methods. Furthermore, we propose a new measure called DGscore that integrates different measures for objective and subjective questions and incorporates a weighting scheme based on type- and question-specific difficulty coefficients to accurately assess the QA performance of each LLM. We validate the Astro-QA dataset through extensive experimentation with 27 open-source and commercial LLMs. The results show that it can serve as a reliable benchmark dataset to evaluate the capacity of LLM in terms of instruction following, knowledge reasoning, and natural language generation in the astronomical domain, which can calibrate current progress and facilitate future research of astronomical LLMs.

In the collaborative filtering (CF) recommendation applications, the sparsity of user rating data, the effectiveness of cold start, the strategy of item information neglection, and user profiles construction are critical to both the efficiency and effectiveness of the recommendation algorithm. In order to solve the above problems, a personalized recommendation approach combining semisupervised support vector machine and active learning (AL) is proposed in this paper, which combines the benefits of both TSVM (Transductive Support Vector Machine) and AL. Firstly, a “maximum-minimum segmentation” of version space-based AL strategy is developed to choose the most informative unlabeled samples for human annotation; it aims to choose the least data which is enough to train a high-quality model. And then, an AL-based semisupervised TSVM algorithm is proposed to make full use of the distribution characteristics of unlabeled samples by adding a manifold regularization into objective function, which is helpful to make the proposed algorithm to overcome the traditional drawbacks of TSVM. Furthermore, during the procedure of recommendation model construction, not only user behavior information and item information, but also demographic information is utilized. Due to the benefits of the above design, the quality of unlabeled sample annotation can be improved; meanwhile, both the data sparsity and cold start problems are alleviated. Finally, the effectiveness of the proposed algorithm is verified based on UCI datasets, and then it is applied to personalized recommendation. The experimental results show the superiority of the proposed method in both effectiveness and efficiency.

In machine learning (ML) problems, it is widely believed that more training samples lead to improved predictive accuracy but incur higher computational costs. Consequently, achieving better data efficiency, that is, the trade-off between the size of the training set and the accuracy of the output model, becomes a key problem in ML applications. In this research, we systematically investigate the data efficiency of Univariate Time Series Anomaly Detection (UTS-AD) models. We first experimentally examine the performance of nine popular UTS-AD algorithms as a function of the training sample size on several benchmark datasets. Our findings confirm that most algorithms become more accurate when more training samples are used, whereas the marginal gain for adding more samples gradually decreases. Based on the above observations, we propose a novel framework called FastUTS-AD that achieves improved data efficiency and reduced computational overhead compared to existing UTS-AD models with little loss of accuracy. Specifically, FastUTS-AD is compatible with different UTS-AD models, utilizing a sampling- and scaling law-based heuristic method to automatically determine the number of training samples a UTS-AD model needs to achieve predictive performance close to that when all samples in the training set are used. Comprehensive experimental results show that, for the nine popular UTS-AD algorithms tested, FastUTS-AD reduces the number of training samples and the training time by 91.09–91.49% and 93.49–93.82% on average without significant decreases in accuracy.

Dear Editor, Dummy attack (DA), a deep stealthy but impactful data integrity attack on power industrial control processes, is recently recognized as hiding the corrupted measurements in normal measurements. In this letter, targeting a more practical case, we aim to detect the one-shot DA, with the purpose of revealing the DA once it is launched. Specifically, we first formulate an optimization problem to generate one-shot DAs. Then, an unsupervised data-driven approach based on a modified local outlier factor (MLOF) is proposed to detect them. To improve the detection performance, the measurements are preprocessed with the gamma transformation and the power patterns are extracted from historical data and integrated into the MLOF algorithm. Finally, extensive experiments are conducted to evaluate the performance of the proposed approach with real-world load data.