Modeling users’ short-term dynamic and long-term static interests to enhance Point-of-Interests (POI) recommendation performance has shown lots of advantages. Since users’ check-in records can be viewed as a graph network, methods based on Graph Neural Networks (GNNs) have recently shown promising applicability for POI recommendation. However, existing GNN-based works have the following shortcomings: (1) ignoring the impact of complex higher-order relationships between user-POI dynamics over time; and (2) ignoring the difference in POI importance that cannot effectively capture the imbalances of geographical influence among POIs. To address these challenges, we propose a novel Self-supervised Long-and Short-term model (SLS-REC) for POI recommendation. Specifically, we first design a spatio-temporal Hawkes attention hypergraph neural network to capture the spatial dependence and temporal evolution in users’ short-term dynamic interests. Then we introduce a dynamic propagation mechanism of GNNs to learn the geographic influences underlying geographic imbalances among POIs. In addition, the contrastive learning framework over a fine-grained node dropout strategy is applied to maximize the mutual information of long and short-term interest representations. Finally, we adaptively unify the recommendation and self-supervised task with an attention-based mechanism to optimize the proposed SLS-REC model for POI recommendation. Experiments on real-world datasets show that the proposed model significantly outperforms state-of-the-art methods.

Powered by advanced information technology, more and more complex systems are exhibiting characteristics of the cyber–physical–social systems (CPSS). In consideration of the cost, legal, and institutional constraints on the study of CPSS in real world, computational experiments have emerged as a new method for quantitative analysis of CPSS. However, with the increase of application scenarios, how to map complex and diverse domain models to artificial society models has become a key challenge to hinder the wide use of computational experiments. In this article, the docking framework between the domain model and the artificial society model was proposed in this article, and the model docking specification is given from three aspects: the agent model, the environmental model, and the rules model. In addition, the effectiveness of the framework was verified by two classic cases: artificial stock market and epidemic prevention and control. The result showed that the proposed model docking framework can provide technical support for the multidisciplinary applications of computational experiments and significantly reduce the difficulty of using the method.

Aspect-level sentiment classification (ASC) seeks to reveal the emotional tendency of a designated aspect of a text. Some researchers have recently tried to exploit large amounts of document-level sentiment classification (DSC) data available to help improve the performance of ASC models through transfer learning. However, these studies often ignore the difference in sentiment distribution between document-level and aspect-level data without preprocessing the document-level knowledge. Our study provides a transfer learning with document-level data augmentation (TL-DDA) framework to transfer more accurate document-level knowledge to the ASC model by means ofdocument-level data augmentationandattention fusion. First, we usedocument data selectionandtext concatenationto produce document-level data with various sentiment distributions. The augmented document data is then utilized for pre-training a well-designed DSC model. Finally, afterattention adjustment, wefuse the word attentionobtained from this DSC model into the ASC model. Results of experiments utilizing two publicly available datasets suggest that TL-DDA is reliable.

Recommender systems have become a crucial intelligent tool, which provides users with personalized services. Graph learning-based recommendation methods treat user-item interactions and the item transitions as pairwise relations but ignore the complex, higher-order interaction information between nodes. Moreover, since most users often interact with few or even no items, graph learning-based recommendation methods suffer from the data sparsity problem, as well as the unbalanced distribution of edges and nodes. To tackle these issues, we propose a Dual Hypergraph-based Self-supervised Learning recommendation model, named DHSL-GM. Specifically, we derive two dual hypergraphs from the user-item bipartite graph, which models the complex high-order user-item interactions by using hypergraph convolution with spectral hypergraph convolution operator. Meanwhile, we design a gated network-based message passing mechanism to dynamically guide message propagation, addressing the problem of the unbalanced distribution of edges and nodes. In addition, to alleviate the data sparsity problem, we design another dual hypergraph convolutional network based on a node discard strategy, which innovatively integrates self-supervised learning into the training of the hypergraph convolutional network. Experimental results on several real datasets demonstrate the superiority and effectiveness of the proposed model.

Session-based recommendation, which uses the interactive information of anonymous users in a period to predict user preferences, has attracted extensive attention. Existing methods mainly exploit the strengths of graph neural networks (GNNs) in capturing structured data features to model complex item transitions for recommendations. However, there still remain some challenges: 1) many methods neglect to exploit cross-session interactions and fail to integrate the latent intra- and inter-session information effectively. 2) some methods barely explore fine-grained contextual factors underlying in the sessions for session-based recommendation, thus failing to capture more comprehensive user preferences. To solve these issues, we propose a novel method called Global Context-Aware Graph Neural Networks (GCA-GNN) which captures the local-session and cross-session preferences respectively, and captures fine-grained global contextual factors to complement user preferences. Specifically, GCA-GNN models user preferences from two different views: the cross-session view and the local-session view. The former learns collaborative user preferences from a cross-session graph, while the latter is designed to learn users' personal preferences from local-session graphs. Furthermore, a context-aware Capsule Graph Neural Network is employed to extract fine-grained contextual factors, serving as complementary information. And we introduce an auxiliary self-supervised learning task to enhance user preferences. Experiments on benchmark datasets demonstrate the strength of our model over the state-of-the-art methods.

Many studies focusing on integrating reviews with ratings to improve recommendation performance have been quite successful. However, these works still face several shortcomings: (1) The importance of dynamically integrating review and interaction data features is typically ignored, yet treating these fusion features equally may lead to an incomplete understanding of user preferences. (2) Some forms of soft attention methods are adopted to model the local semantic information of words. As features thus captured may contain irrelevant information, the generated attention map is neither discriminatory nor detailed. In this paper, we propose a novelAdaptiveHierarchicalAttention-enhancedGated network integrating reviews for item recommendation, named AHAG. AHAG is a unified framework to capture the hidden intentions of users by adaptively incorporating reviews. Specifically, we design a gated network to dynamically fuse the extracted features and select the features that are most relevant to user preferences. To capture distinguishing fine-grained features, we introduce a hierarchical attention mechanism to learn important semantic information features and the dynamic interaction of these features. Besides, the high-order non-linear interaction of neural factorization machines is utilized to derive the rating prediction. Experiments on seven real-world datasets show that the proposed AHAG significantly outperforms state-of-the-art methods. Furthermore, the attention mechanism can highlight the relevant information in reviews to increase the interpretability of the recommendation task. Source codes are available inhttps://github.com/luojia527/AHAG.

Recently, the application of deep reinforcement learning into the field of session-based interactive recommendation has attracted great attention from researchers. However, despite that some interactive recommendation models based on deep reinforcement learning have been proposed, they still suffer to the following limitations: (1) these works ignore the skip behaviors of sequential patterns in users' clicking behavior; (2) these works fail to incorporate positive feedback and negative feedback into the proposed deep reinforcement recommender system when the positive feedback is sparse. Therefore, to solve the problems mentioned above, a novel Deep Q-Network based recommendation framework incorporating CNN and GAN-based models is proposed to acquire robust performance, named DRCGR. Specifically, in DRCGR, a CNN model is used to capture the sequential features for positive feedback. Then, an adversarial training is adopted to learn optimal negative feedback representations Then, positive/negative representations are fed into DQN simultaneously, which are conducive to generating better action-value function The experimental results based on real-world e-commerce data demonstrate our framework's superiority over some state-of-the-art recommendation models.

Despite the great success of many matrix factorization based collaborative filtering approaches, there is still much space for improvement in recommender system field. One main obstacle is the cold-start and data sparseness problem, requiring better solutions. Recent studies have attempted to integrate review information into rating prediction. However, there are two main problems: (1) most of existing works utilize a static and independent method to extract the latent feature representation of user and item reviews ignoring the correlation between the latent features, which may fail to capture the preference of users comprehensively. (2) there is no effective framework that unifies ratings and reviews. Therefore, we propose a novel d ual a ttention m utual l earning between ratings and reviews for item recommendation, named DAML. Specifically, we utilize local and mutual attention of the convolutional neural network to jointly learn the features of reviews to enhance the interpretability of the proposed DAML model. Then the rating features and review features are integrated into a unified neural network model, and the higher-order nonlinear interaction of features are realized by the neural factorization machines to complete the final rating prediction. Experiments on the five real-world datasets show that DAML achieves significantly better rating prediction accuracy compared to the state-of-the-art methods. Furthermore, the attention mechanism can highlight the relevant information in reviews to increase the interpretability of rating prediction.

In change detection analysis, it is important to distinguish the real change targets and pseudo change targets accurately. Supervised change detection has been regarded as the best way to reduce the effects of pseudo change information. This is because human visual system has the ability to find the real changes. By imitating human visual characteristic, visual attention mechanism can bring the improvement of accuracy and speed of unsupervised change detection. In this paper, a change detection approach based on visual attention mechanism is proposed to reduce the influence of pseudo change information. Experiments show that the proposed method significantly reduces the false alarm rate and missed alarm rate and also shows insensitive to noise.

In this paper, a new package-group-transmission-based algorithm is proposed for human activity recognition in videos. The proposed algorithm first models the entire scene as a network where each node in the network corresponds to a segmentation of the scene. Based on this network, we further model people in the scene as groups of packages. Thus, various human activities can be modeled as the process of "package group transmission" in the network and these activities can be efficiently recognized by suitably analyzing the "package transmission" process. Our proposed algorithm can not only detect activities under the challenging multiple camera scenario, but also be able to recognize various complex group activities among people. Experimental results demonstrate the effectiveness of our proposed algorithm.