MvFS: Multi-view Feature Selection for Recommender System

Feature selection, which is a technique to select key features in recommender systems, has received increasing research attention. Recently, Adaptive Feature Selection (AdaFS) has shown remarkable performance by adaptively selecting features for each data instance, considering that the importance of a given feature field can vary significantly across data. However, this method still has limitations in that its selection process could be easily biased to major features that frequently occur. To address these problems, we propose Multi-view Feature Selection (MvFS), which selects informative features for each instance more effectively. Most importantly, MvFS employs a multi-view network consisting of multiple sub-networks, each of which learns to measure the feature importance of a part of data with different feature patterns. By doing so, MvFS mitigates the bias problem towards dominant patterns and promotes a more balanced feature selection process. Moreover, MvFS adopts an effective importance score modeling strategy which is applied independently to each field without incurring dependency among features. Experimental results on real-world datasets demonstrate the effectiveness of MvFS compared to state-of-the-art baselines.Comment: CIKM 202

Characteristics of Solar Wind Density Depletions During Solar Cycles 23 and 24

Solar wind density depletions are phenomena that solar wind density is rapidly decreased and keep the state. They are generally believed to be caused by the interplanetary (IP) shocks. However, there are other cases that are hardly associated with IP shocks. We set up a hypothesis for this phenomenon and analyze this study. We have collected the solar wind parameters such as density, speed and interplanetary magnetic field (IMF) data related to the solar wind density depletion events during the period from 1996 to 2013 that are obtained with the advanced composition explorer (ACE) and the Wind satellite. We also calculate two pressures (magnetic, dynamic) and analyze the relation with density depletion. As a result, we found total 53 events and the most these phenomena’s sources caused by IP shock are interplanetary coronal mass ejection (ICME). We also found that solar wind density depletions are scarcely related with IP shock’s parameters. The solar wind density is correlated with solar wind dynamic pressure within density depletion. However, the solar wind density has an little anti-correlation with IMF strength during all events of solar wind density depletion, regardless of the presence of IP shocks. Additionally, In 47 events of IP shocks, we find 6 events that show a feature of blast wave. The quantities of IP shocks are weaker than blast wave from the Sun, they are declined in a short time after increasing rapidly. We thus argue that IMF strength or dynamic pressure are an important factor in understanding the nature of solar wind density depletion. Since IMF strength and solar wind speed varies with solar cycle, we will also investigate the characteristics of solar wind density depletion events in different phases of solar cycle as an additional clue to their physical nature

Operational Dst index prediction model based on combination of artificial neural network and empirical model

In this paper, an operational Dst index prediction model is developed by combining empirical and Artificial Neural Network (ANN) models. ANN algorithms are widely used to predict space weather conditions. While they require a large amount of data for machine learning, large-scale geomagnetic storms have not occurred sufficiently for the last 20 years, Advanced Composition Explorer (ACE) and Deep Space Climate Observatory (DSCOVR) mission operation period. Conversely, the empirical models are based on numerical equations derived from human intuition and are therefore applicable to extrapolate for large storms. In this study, we distinguish between Coronal Mass Ejection (CME) driven and Corotating Interaction Region (CIR) driven storms, estimate the minimum Dst values, and derive an equation for describing the recovery phase. The combined Korea Astronomy and Space Science Institute (KASI) Dst Prediction (KDP) model achieved better performance contrasted to ANN model only. This model could be used practically for space weather operation by extending prediction time to 24 h and updating the model output every hour