Ranking ensemble is a critical component in real recommender systems. When a user visits a platform, the system will prepare several item lists, each of which is generally from a single behavior objective recommendation model. As multiple behavior intents, e.g., both clicking and buying some specific item category, are commonly concurrent in a user visit, it is necessary to integrate multiple single-objective ranking lists into one. However, previous work on rank aggregation mainly focused on fusing homogeneous item lists with the same objective while ignoring ensemble of heterogeneous lists ranked with different objectives with various user intents.

Overfitting is a common problem in machine learning, which means the model too closely fits the training data while performing poorly in the test data. Among various methods of coping with overfitting, dropout is one of the representative ways. From randomly dropping neurons to dropping neural structures, dropout has achieved great success in improving model performances. Although various dropout methods have been designed and widely applied in past years, their effectiveness, application scenarios, and contributions have not been comprehensively summarized and empirically compared by far. It is the right time to make a comprehensive survey. In this paper, we systematically review previous dropout methods and classify them into three major categories according to the stage where dropout operation is performed. Specifically, more than seventy dropout methods published in top AI conferences or journals (e.g., TKDE, KDD, TheWebConf, SIGIR) are involved. The designed taxonomy is easy to understand and capable of including new dropout methods. Then, we further discuss their application scenarios, connections, and contributions. To verify the effectiveness of distinct dropout methods, extensive experiments are conducted on recommendation scenarios with abundant heterogeneous information. Finally, we propose some open problems and potential research directions about dropout that worth to be further explored.

Since the magnetic field distribution in winding window of thin film micro-inductor with closed magnetic core has obvious 2-D characteristics, the accuracy of 1-D analytical model for its winding AC losses is poor. The magnetic field of the micro-inductor is analyzed deeply to show that its distribution along the winding edge does not change with frequency. According to the analysis result, the magnetic field strength around the winding edge is calculated with the Ampere's law. And the 2-D magnetic field is decomposed into an x component and y component in rectangular coordinates. Then based on the superposition and uniqueness law of magnetic field, a 2-D analytical winding losses model of thin film micro-inductor is proposed. Finally, the finite element analysis (FEA) simulation verified the loss model to have high accuracy under wide exciting current frequency and the loss model can be used easily.