Collaborative filtering (CF) is an essential technique in recommender systems that provides personalized recommendations by only leveraging user-item interactions. However, most CF methods represent users and items as fixed points in the latent space, lacking the ability to capture uncertainty. While probabilistic embedding is proposed to intergrate uncertainty, they suffer from several limitations when introduced to graph-based recommender systems. Graph convolutional network framework would confuse the semantic of uncertainty in the nodes, and similarity measured by Kullback–Leibler (KL) divergence suffers from degradation problem and demands an exponential number of samples. To address these challenges, we propose a novel approach, called the Wasserstein dependent Graph ATtention network (W-GAT), for collaborative filtering with uncertainty. We utilize GAT and Wasserstein distance to learn Gaussian embedding for each user and item. Additionally, our method incorporates Wasserstein-dependent mutual information further to increase the similarity between positive pairs. Experimental results on three benchmark datasets show the superiority of W-GAT compared to several representative baselines. Extensive experimental analysis validates the effectiveness of W-GAT in capturing uncertainty by modeling the range of user preferences and categories associated with items.

Graph neural networks (GNNs) become popular in recommender systems treating the interaction data of user and item as a bipartite graph. Recently, graph contrastive learning achieves superior results for collaborative filtering by reinforcing the learned representations by generating contrastive views through data augmentation. Despite their successful application in recommendation scenarios, there is still some room for improvement: most of these methods perform data augmentation from the data perspective, and the model potential is not exploited enough because more contrastive perspectives are not considered; negative sample bias caused by the different degrees of nodes exists in the contrastive loss. In this paper, we propose a Siamese Adaptive Contrastive learning framework (SACL) to mitigate these issues. Our model utilizes Siamese network as a small perturbation to the model and combines it with data augmentation to learn more robust representations and realizes adaptive contrastive learning introducing the common neighbors’ information of users and items to weight negative samples. Experiments on several public datasets show better performance of our model compared to existing representative methods.

Explanations play an essential role in helping users evaluate results from recommender systems. Various natural language generation methods have been proposed to generate explanations for the recommendation. However, they usually suffer from two problems. First, since user-provided review text contains noisy data, the generated explanations may be irrelevant to the recommended items. Second, as lacking some supervision signals, most of the generated sentences are similar, which cannot meet the diversity and personalized needs of users. To tackle these problems, we propose a multimodal contrastive transformer (MMCT) model for an explainable recommendation, which incorporates multimodal information into the learning process, including sentiment features, item features, item images, and refined user reviews. Meanwhile, we propose a dynamic fusion mechanism during the decoding stage, which generates supervision signals to guide the explanation generation. Additionally, we develop a contrastive objective to generate diverse explainable texts. Comprehensive experiments on two real-world datasets show that the proposed model outperforms comparable explainable recommendation baselines in terms of explanation performance and recommendation performance. Efficiency analysis and robustness analysis verify the advantages of the proposed model. While ablation analysis establishes the relative contributions of the respective components and various modalities, the case study shows the working of our model from an intuitive sense.

Collaborative filtering (CF) is the basic method for recommendation with implicit feedback. Recently, various state-of-the-art CF integrates graph neural networks. However, they often suffer from popularity bias, causing recommendations to deviate from users' genuine preferences. Additionally, several contrastive learning methods based on the in-batch sample strategy have been proposed to train the CF model effectively, but they are prone to suffering from sample bias. To address this problem, debiased contrastive loss has been employed in the recommendation, but instead of personalized debiasing, it treats each user equally. In this paper, we propose a popularity-aware debiased contrastive loss for CF, which can adaptively correct the positive and negative scores based on the popularity of users and items. Our approach aims to reduce the negative impact of popularity and sample bias simultaneously. We theoretically analyze the effectiveness of the proposed method and reveal the relationship between popularity and gradient, which justifies the correction strategy. We extensively evaluate our method on three public benchmarks over balanced and imbalanced settings. The results demonstrate its superiority over the existing debiased strategies, not only on the entire datasets but also when segmenting the datasets based on item popularity.

Personalized recommendation plays a central role in various online applications. To provide quality recommendation service, it is of crucial importance to consider multi-modal information associated with users and items, e.g., review text, description text, and images. However, many existing approaches do not fully explore and fuse multiple modalities. To address this problem, we propose a multi-modal contrastive pre-training model for recommendation. We first construct a homogeneous item graph and a user graph based on the relationship of co-interaction. For users, we propose intra-modal aggregation and inter-modal aggregation to fuse review texts and the structural information of the user graph. For items, we consider three modalities: description text, images, and item graph. Moreover, the description text and image complement each other for the same item. One of them can be used as promising supervision for the other. Therefore, to capture this signal and better exploit the potential correlation of intra-modalities, we propose a self-supervised contrastive inter-modal alignment task to make the textual and visual modalities as similar as possible. Then, we apply inter-modal aggregation to obtain the multi-modal representation of items. Next, we employ a binary cross-entropy loss function to capture the potential correlation between users and items. Finally, we fine-tune the pre-trained multi-modal representations using an existing recommendation model. We have performed extensive experiments on three real-world datasets. Experimental results verify the rationality and effectiveness of the proposed method.

With the frequent occurrence of unconventional global emergencies, the public security field has received more and more attention. As an unconventional emergency, terrorist attacks have aroused global attention. So, how should we extract useful information from a large number of terrorist attacks and find the law of the attack, so that we can effectively prevent or take early measures to reduce losses? To this end, we are based on the Global Terrorism Database (GTD), and aim to predict the next province or state a terrorist organization may attack at a specific time point by mining the terrorist organizations' historical records and other types of information availabl, such as incident information and so on. Then, Based on these incident information and spatiotemporal information, we propose a neural network called ATtention-based Fused-SpatialTemporal LSTM (ATFST-LSTM) to predict the next location which may be attacked. We test the efficiency of our models on GTD, experiments show that our models has achieved better results.