CME is one of the important events in the sun-earth system as it can induce geomagnetic disturbance and an associated space environment effect. It is of special significance to predict whether CME will reach the Earth and when it will arrive. In this paper, we firstly built a new multiple association list for 215 different events with 18 characteristics including CME features, eruption region coordinates and solar wind parameters. Based on the CME list, we designed a novel model based on the principle of the recommendation algorithm to predict the arrival time of CMEs. According to the two commonly used calculation methods in the recommendation system, cosine distance and Euclidean distance, a controlled trial was carried out respectively. Every feature has been found to have its own appropriate weight. The error analysis indicates the result using the Euclidean distance similarity is much better than that using cosine distance similarity. The mean absolute error and root mean square error of test data in the Euclidean distance are 11.78 and 13.77 h, close to the average level of other CME models issued in the CME scoreboard, which verifies the effectiveness of the recommendation algorithm. This work gives a new endeavor using the recommendation algorithm, and is expected to induce other applications in space weather prediction.

Coronal mass ejections (CMEs) are one of the major disturbance sources of space weather. Therefore, it is of great significance to determine whether CMEs will reach the earth. Utilizing the method of logistic regression, we first calculate and analyze the correlation coefficients of the characteristic parameters of CMEs. These parameters include central position angle, angular width, and linear velocity, which are derived from the Large Angle and Spectrometric Coronagraph (LASCO) images. We have developed a logistic regression model to predict whether a CME will reach the earth, and the model yields an F1 score of 30% and a recall of 53%. Besides, for each CME, we use the recommendation algorithm to single out the most similar historical event, which can be a reference to forecast CMEs geoeffectiveness forecasting and for comparative analysis.