Outliers are intrinsic to recommender systems (RSs) due to user uncertainty and bring large deviations to the total loss. Classical loss functions such as$L_{1}$and$L_{2}$do not consider this issue, thus biasing the model toward abnormal ratings. Advanced ones such as Logcosh, Huber, and Wing losses try to control model biasing; however, they do not consider the rating ranges of recommender systems. In this article, we propose a Seagull loss combining quadratic and softsign functions to handle this issue. The quadratic function aims to preserve or appropriately increase losses caused by small deviations, while softsign function aims to suppress losses caused by large deviations. It is distinguished from three types of popular losses. Compared with$L_{1}$and$L_{2}$, it controls the upper bound, therefore suppressing the influence of outliers. Compared with Logcosh and Huber, it not only adjusts the loss of small deviations but also suppresses the loss of large deviations. Compared with Wing loss, it considers the characteristics of data in RSs and therefore is more suitable to the matrix factorization model. Experiments are undertaken on six real-world recommendation datasets in comparison with six popular loss functions. Results show that Seagull loss function has superiority to all losses above on mean absolute error (MAE) and root mean square error (RMSE) and slight inferiority to$L_{2}$and Wing on hit ratio (HR), mean average precision (MAP), and normalized discounted cumulative gain (NDCG).

In practical scenarios, abnormal network traffic detection often requires analysis of massive, high-dimensional, and unbalanced data. Popular detection methods waste time by processing each data stream separately. In this paper, we propose a multi-granularity abnormal network traffic detection algorithm based on multi-instance learning to address this issue. The bag generation technique randomly assembles a predetermined number of data packets into a bag. The bag mapping technique encodes each bag into a new feature space through clustering. The multi-granularity classification technique filters normal data efficiently at the bag granularity before detecting threats at the instance granularity. Experiments were carried out on five datasets in comparison to three state-of-the-art algorithms. Compared with the competing methods, the results show that the average efficiency of this method is increased by more than 10 ∼ 20 times, and the accuracy is slightly lower by 0.1 ∼ 0.8%.

Label distribution learning (LDL) employs probabilistic labels to capture the varying degrees of relevance among decision attributes. Existing LDL algorithms usually employ sample correlations to improve their predictive validity. However, they merely employ the superficial features of the sample for correlation analysis, seldom delving into its latent deep features. In this paper, we propose an algorithm to explore the sample topology (ST-LDL) to address this problem. First, we construct a locally weighted directed graph for each target sample. The target sample and its k neighbors are regarded as nodes within the graph. The asymmetrical correlation among these $k+1$ samples is computed separately to determine the weight value of the graph. Then, we utilise the local topology between samples to mine latent information and reconstruct the samples. The local graph is fed into the graph convolutional network to nonlinearly reconstruct the features by mining the latent information. Finally, we designed a new optimization objective function for the reconstructed samples. Experiments are carried out on elven real-world datasets in comparison with seven state-of-the-art algorithms. The results show that our algorithm outperforms several other algorithms, proving the effectiveness of our algorithm.

Player behavior modeling is of the utmost importance in game development and player matching. This problem is challenging because behavior is multi-semantic and hard to represent. Existing work often suffers from low generalization ability and high demand for supervisory information. In this paper, we present a behavior representation learning method (BRL) for Reversi players. It learns entirely from unlabeled game records. First, we develop an asymmetric encoder-decoder architecture to learn the mapping between states and actions. The encoder maps game records into a latent subspace for behavior representation. Second, we mask random states to force the encoder to capture the high-level features of the policy. The decoder predicts the corresponding actions according to the latent representation. Coupling these two designs, the semantic relevance of behaviors can be more effectively measured. Experiments were conducted in Reversi, using 14,000 game records of different players to learn behavior representations. Transfer performance in downstream tasks outperforms the supervised method and shows promising scaling ability. This work opens a new way for analyzing and modeling player behavior.

Label distribution learning (LDL) can more accurately represent the degree of correlation between labels and samples than multi-label learning. However, LDL usually has limited available label data, which is not conducive to training deep learning models. Data augmentation refers to methods for solving limited data problems by introducing unobserved data or latent variables to increase the size and quality of the training dataset. In this paper, we augment the dataset by mapping the features and label distributions of the generated samples to the same subspace, and using the generator to learn the distribution of the original data in this space. First, we use the encoder and generator to extract effective information from sample features and label distributions, respectively. Second, we randomly fuse the existing label distribution to generate a new label distribution, then map it to the subspace and restore the corresponding features through the decoder. Finally, these generated samples are mixed with the original training set to train a model for predicting label distribution. Experimental results on nine real-world datasets show that our proposed algorithm can improve the performance of deep learning models to a certain extent.