In this paper, a large-scale multi-objective gate assignment model is constructed by considering the flight international and domestic attributes, task type, airline affiliation, and aircraft type. Then a multi-objective quantum-inspired evolutionary algorithm based on decomposition mechanism, namely MOQEA/D is developed to solve the constructed model effectively. Specifically, a new decomposition mechanism is designed to decompose the multi-objective GAP into several single-objective sub-GAPs. Each quantum bit string solves a single-objective sub-GAP independently. And a new optimal crossover strategy is proposed to limit the randomness of observation operations and maximize the preservation of excellent genes to further improve the optimization performance. Finally, the multi-objective knapsack problem and the multi-objective GAP are selected to verify the effectiveness of the MOQEA/D. The experiment results demonstrate that the MOQEA/D can effectively solve large-scale multi-objective knapsack problem and obtain ideal gate assignment results. It takes on very significance and application value in solving complex optimization problems.

Recent years have witnessed the explosive growth of interaction behaviors in multimedia information systems, where multi-behavior recommender systems have received increasing attention by leveraging data from various auxiliary behaviors such as tip and collect. Among various multi-behavior recommendation methods, non-sampling methods have shown superiority over negative sampling methods. However, two observations are usually ignored in existing state-of-the-art non-sampling methods based on binary regression: (1) users have different preference strengths for different items, so they cannot be measured simply by binary implicit data; (2) the dependency across multiple behaviors varies for different users and items. To tackle the above issue, we propose a novel non-sampling learning framework namedCriterion-guidedHeterogeneousCollaborativeFiltering (CHCF). CHCF introduces both upper and lower thresholds to indicate selection criteria, which will guide user preference learning. Besides, CHCF integrates criterion learning and user preference learning into a unified framework, which can be trained jointly for the interaction prediction of the target behavior. We further theoretically demonstrate that the optimization of Collaborative Metric Learning can be approximately achieved by the CHCF learning framework in a non-sampling form effectively. Extensive experiments on three real-world datasets show the effectiveness of CHCF in heterogeneous scenarios.

Semi-supervised node classification is a crucial challenge in relational data mining and has attracted increasing interest in research on graph neural networks (GNNs). However, previous approaches merely utilize labeled nodes to supervise the overall optimization, but fail to sufficiently explore the information of their underlying label distribution. Even worse, they often overlook the robustness of models, which may cause instability of network outputs to random perturbations. To address the aforementioned shortcomings, we develop a novel framework termed Hybrid Curriculum Pseudo-Labeling (HCPL) for efficient semi-supervised node classification. Technically, HCPL iteratively annotates unlabeled nodes by training a GNN model on the labeled samples and any previously pseudo-labeled samples, and repeatedly conducts this process. To improve the model robustness, we introduce a hybrid pseudo-labeling strategy that incorporates both prediction confidence and uncertainty under random perturbations, therefore mitigating the influence of erroneous pseudo-labels. Finally, we leverage the idea of curriculum learning to start from annotating easy samples, and gradually explore hard samples as the iteration grows. Extensive experiments on a number of benchmarks demonstrate that our HCPL beats various state-of-the-art baselines in diverse settings.

Graph clustering has been a hot research topic and is widely used in many fields, such as community detection in social networks. Lots of works combining auto-encoder and graph neural networks have been applied to clustering tasks by utilizing node attributes and graph structure. These works usually assumed the inherent parameters (i.e., size and variance) of different clusters in the latent embedding space are homogeneous, and hence the assigned probability is monotonous over the Euclidean distance between node embeddings and centroids. Unfortunately, this assumption usually does not hold since the size and concentration of different clusters can be quite different, which limits the clustering accuracy. In addition, the node embeddings in deep graph clustering methods are usually L2 normalized so that it lies on the surface of a unit hyper-sphere. To solve this problem, we proposed D eep A daptive G raph C lustering via von Mises-Fisher distributions, namely DAGC. DAGC assumes the node embeddings H can be drawn from a von Mises-Fisher distribution and each cluster k is associated with cluster inherent parameters ρ k which includes cluster center μ and cluster cohesion degree κ. Then we adopt an EM-like approach (i.e., 𝒫( H | ρ ) and 𝒫( ρ | H ), respectively) to learn the embedding and cluster inherent parameters alternately. Specifically, with the node embeddings, we proposed to update the cluster centers in an attraction-repulsion manner to make the cluster centers more separable. And given the cluster inherent parameters, a likelihood-based loss is proposed to make node embeddings more concentrated around cluster centers. Thus, DAGC can simultaneously improve the intra-cluster compactness and inter-cluster heterogeneity. Finally, extensive experiments conducted on four benchmark datasets have demonstrated that the proposed DAGC consistently outperforms the state-of-the-art methods, especially on imbalanced datasets.

Nearest neighbor search aims at obtaining the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining. Hashing is one of the most widely used methods for its computational and storage efficiency. With the development of deep learning, deep hashing methods show more advantages than traditional methods. In this survey, we detailedly investigate current deep hashing algorithms including deep supervised hashing and deep unsupervised hashing. Specifically, we categorize deep supervised hashing methods into pairwise methods, ranking-based methods, pointwise methods as well as quantization according to how measuring the similarities of the learned hash codes. Moreover, deep unsupervised hashing is categorized into similarity reconstruction-based methods, pseudo-label-based methods, and prediction-free self-supervised learning-based methods based on their semantic learning manners. We also introduce three related important topics including semi-supervised deep hashing, domain adaption deep hashing, and multi-modal deep hashing. Meanwhile, we present some commonly used public datasets and the scheme to measure the performance of deep hashing algorithms. Finally, we discuss some potential research directions in conclusion.

Graph clustering aiming to partition nodes into several disjoint subsets is a fundamental task for graph-structured learning. Traditional graph clustering methods only consider the adjacency information. In recent years, inspired by the homophily assumption that the adjacent nodes tend to have similar features and labels, most existing graph clustering approaches leverage node attribute information to improve graph clustering performance. These works have mainly focused on node embedding learning via the various combinations of auto-encoder and graph neural networks. As for clustering learning, they introduce a self-optimizing strategy that assumes that all clusters are homogeneous. However, this assumption usually does not hold since the size and variance of different clusters can be quite different, and self-optimizing strategy is incompetent in dealing with this heterogeneous clusters. In this work, we propose a novel method named Adaptive Harmony Learning and Optimization (AHLO) for attributed graph clustering, which models the node embeddings with the mixture of von Mises-Fisher distributions on the unit hypersphere and develops an alternating learning strategy. Specifically, we take the node embeddings as the supervisory signals for the update of the mixture parameters, and the mixture distribution as the supervisory signals for the update of the node embeddings. To prevent small clusters from annexing by large clusters, we develop the regularized harmony loss to enhance the prediction on small clusters. In the mixture parameter optimization stage, we utilize EM algorithm and heuristically design a center update scheme with consideration of the posterior probability confidence and the impact of other centers. Hence, AHLO can simultaneously improve the intra-cluster compactness and inter-cluster separability. Extensive experiments on four benchmark attributed graph datasets have demonstrated the effectiveness of our proposed AHLO.

Rapid and accurate prediction of user preferences is the ultimate goal of today’s recommender systems. More and more researchers pay attention to multi-behavior recommender systems which utilize the auxiliary types of user-item interaction data, such as page view and add-to-cart to help estimate user preferences. Recently, graph-based methods were proposed to showcase an advanced capability in representation learning and capturing collaborative signals. However, we argue that these methods ignore the intrinsic difference between the two types of nodes in the bipartite graph and aggregate information from neighboring nodes with the same functions. Besides, these models do not fully explore the collaborative signals implied by the meta-path across different types of behavior, which causes a huge loss of the potential semantic information across behaviors. To address the above limitations, we present a unified graph model named SaGCN (short for Semantic-aware Graph Convolutional Networks). Specifically, we construct separate user-user and item-item graphs by meta-path, and apply separate aggregation and transformation functions to propagate user and item information. To perform better semantic propagation, we design a relation composition function and a semantic propagation architecture for heterogeneous collaborative filtering signals learning. Extensive experiments on two real-world datasets show that SaGCN outperforms a wide range of state-of-the-art methods in multi-behavior scenarios.

Hashing has been widely used in approximate nearest neighbor search recently. Deep supervised hashing methods are not widely-used because of the lack of labeled data, especially when the domain is transferred. Meanwhile, unsupervised deep hashing models can hardly achieve satisfactory performance due to the lack of reliable similarity signals. Here, we propose a novel deep unsupervised hashing method, namely Distilled Smooth Guidance (DSG), which can learn a distilled dataset consisting of similarity signals as well as smooth confidence signals. Specifically, we obtain the similarity confidence weights based on the initial noisy similarity signals learned from local structures and construct a priority loss function for smooth similarity-preserving learning. Besides, global information based on clustering is utilized to distill the image pairs by removing contradictory similarity signals. Extensive experiments on three widely used bench-mark datasets show that the proposed DSG consistently out-performs the state-of-the-art search methods.

Hashing is widely-used in approximate nearest neighbor search for its computational efficiency. Most of the existing unsupervised hashing methods are based on local consistency that the Hamming distance between two images should be small if their features are similar. However, many false similar pairs may be included for the insufficient representation of features. Here we proposed deep unsupervised hashing by Global and Local Consistency (GLC). Specifically, GLC has two components named semantic information generating and semantic consistency learning, and each component is conducted from both global and local views. From local view, GLC introduces reliable graph and penalty graph to capture local signals with high confidence to preserve the semantic structure. From global view, GLC includes a distribution loss to capture the global consistency with cluster signals. Extensive experimental results on three widely-used benchmark datasets show that GLC performs better than existing state- of-the-art methods.

Recently, hashing is widely used in approximate nearest neighbor search for its storage and computational efficiency. Most of the unsupervised hashing methods learn to map images into semantic similarity-preserving hash codes by constructing local semantic similarity structure from the pre-trained model as the guiding information, i.e., treating each point pair similar if their distance is small in feature space. However, due to the inefficient representation ability of the pre-trained model, many false positives and negatives in local semantic similarity will be introduced and lead to error propagation during the hash code learning. Moreover, few of the methods consider the robustness of models, which will cause instability of hash codes to disturbance. In this paper, we propose a new method named Comprehensive sImilarity Mining and cOnsistency learNing (CIMON). First, we use global refinement and similarity statistical distribution to obtain reliable and smooth guidance. Second, both semantic and contrastive consistency learning are introduced to derive both disturb-invariant and discriminative hash codes. Extensive experiments on several benchmark datasets show that the proposed method outperforms a wide range of state-of-the-art methods in both retrieval performance and robustness.

With the increasing scale and diversification of interaction behaviors in E-commerce, more and more researchers pay attention to multi-behavior recommender systems which utilize interaction data of other auxiliary behaviors. However, all existing models ignore two kinds of intrinsic heterogeneity which are helpful to capture the difference of user preferences and the difference of item attributes. First (intra-heterogeneity), each user has multiple social identities with otherness, and these different identities can result in quite different interaction preferences. Second (inter-heterogeneity), each item can transfer an item-specific percentage of score from low-level behavior to high-level behavior for the gradual relationship among multiple behaviors. Thus, the lack of consideration of these heterogeneities damages recommendation rank performance. To model the above heterogeneities, we propose a novel method named intrA- and inteR-heteroGeneity recOmmendation model (ARGO). Specifically, we embed each user into multiple vectors representing the user's identities, and the maximum of identity scores indicates the interaction preference. Besides, we regard the item-specific transition percentage as trainable transition probability between different behaviors. Extensive experiments on two real-world datasets show that ARGO performs much better than the state-of-the-art in multi-behavior scenarios.

The majority of deep unsupervised hashing methods usually first construct pairwise semantic similarity information and then learn to map images into compact hash codes while preserving the similarity structure, which implies that the quality of hash codes highly depends on the constructed semantic similarity structure. However, since the features of images for each kind of semantics usually scatter in high-dimensional space with unknown distribution, previous methods could introduce a large number of false positives and negatives for boundary points of distributions in the local semantic structure based on pairwise cosine distances. Towards this limitation, we propose a general distribution-based metric to depict the pairwise distance between images. Specifically, each image is characterized by its random augmentations that can be viewed as samples from the corresponding latent semantic distribution. Then we estimate the distances between images by calculating the sample distribution divergence of their semantics. By applying this new metric to deep unsupervised hashing, we come up with Distribution-based similArity sTructure rEconstruction (DATE). DATE can generate more accurate semantic similarity information by using non-parametric ball divergence. Moreover, DATE explores both semantic-preserving learning and contrastive learning to obtain high-quality hash codes. Extensive experiments on several widely-used datasets validate the superiority of our DATE.