Accurate interpretation of Notices To Airmen (NOTAMs) is critical for aviation safety, yet their condensed and cryptic language poses significant challenges to both manual and automated processing. Existing automated systems are typically limited to "Shallow Parsing," failing to extract the actionable intelligence needed for operational decisions. We formalize the complete interpretation task as "Deep Parsing," a dual-reasoning challenge requiring both dynamic knowledge grounding (linking the NOTAM to evolving real-world aeronautical data) and schema-based inference (applying static domain rules to deduce operational status). To tackle this challenge, we propose NOTAM-Evolve, a self-evolving framework that enables a Large Language Model (LLM) to autonomously master complex NOTAM interpretation. Leveraging a knowledge graph-enhanced retrieval module for data grounding, the framework introduces a crucial closed-loop learning process where the LLM progressively improves from its own outputs, minimizing the need for extensive human-annotated reasoning traces. In conjunction with this framework, we introduce a new benchmark dataset of 10,000 expert-annotated NOTAMs. Our experiments demonstrate that NOTAM-Evolve achieves a 30.4% absolute accuracy improvement over the base LLM, establishing a new state-of-the-art on the task of structured NOTAM interpretation.

Graph-based recommender systems have become prevalent in micro-video recommendation by modeling user-item interactions as a bipartite graph. However, these methods face two inherent limitations: (1) their reliance on a fixed, pre-defined graph structure makes them susceptible to noisy interactions, and (2) the multi-modal representations they learn often contain redundant information that is not discriminative enough for the recommendation task. To overcome these issues, we propose a novel Multi-modal Bipartite Graph Structure Learning network (MBGSL), which leverages the information bottleneck principle for robust micro-video recommendation. Specifically, MBGSL first learns adaptive graph structures from multi-modal content (e.g., visual, acoustic, textual) through dedicated graph learners to mitigate noise. Then, it applies an intra-modality information bottleneck to learn minimal sufficient representations within each modality and an inter-modality information bottleneck to capture distinctive information across modalities, thereby eliminating redundancy. Furthermore, the model incorporates collaborative signals through a contrastive learning objective to guide the graph structure learning process. Extensive experiments on three real-world datasets demonstrate that MBGSL achieves state-of-the-art performance, significantly surpassing existing baselines.

Micro-videos, characterized by their brief duration and rich multi-modal content, have become increasingly popular on digital platforms. Micro-Video Popularity Prediction (MVPP) is crucial for understanding user behavior, identifying emerging trends, and leveraging commercial opportunities. While existing MVPP methods predominantly focus on extracting features from the multi-modal content of micro-videos, they often overlook the valuable social interactions that occur within micro-video platforms. In this paper, we introduce a novel Social-Aware Multimodal Graph Learning (SAMGL) approach that integrates multi-modal content and social information to enhance MVPP. First, we propose Social-Aware Multimodal Precomputation (SAMP) to efficiently utilize complete graph information in micro-video popularity prediction, aggregating multi-modal signals to mitigate information loss. Second, we introduce Retrieval Augmentation Prediction (RAP), which retrieves similar micro-videos to enhance prediction by incorporating global content features beyond direct graph connections. To facilitate our approach, we construct the datasets by incorporating social interactions as graph information. Extensive experiments on the datasets demonstrate that our model achieves state-of-the-art performance, validating the effectiveness of integrating social information and the proposed local-global aggregation mechanisms.

Text-Attributed Heterogeneous Graphs (TAHGs), which combine text data with various graph relationship information linked to rich semantic entities, are ubiquitous in real-world scenarios. To extract information from TAHGs, a commonly used method is employing Pretrained Language Models (PLMs). However, existing methods are primarily designed for processing text and face challenges when dealing with graph information, leading to two main issues: incomplete context due to graph sampling and weak integration of text and graph information. In this article, we present a new approach named Metapath-Enhanced Language Model Pretraining (MLMP) on Text-Attributed Heterogeneous Graphs. The proposed model starts by gathering metapath information through pre-computed neighbor aggregation using a simple mean aggregator. Subsequently, this gathered metapath information, combined with textual data, is input into a GNN-nested PLM. Here, GNN components at each layer are nested alongside the transformer blocks of PLMs during the training process. We have also developed corresponding pretraining strategies for joint pretraining. The experimental results indicate that our model efficiently captures information within TAHGs. Across benchmark datasets, it consistently outperforms current state-of-the-art methods, demonstrating remarkable effectiveness in tasks such as link prediction and node classification. Our code is available at https://github.com/chensh911/MLMP .

Multimedia recommendation systems leverage user–item interactions and multimodal information to capture user preferences, enabling more accurate and personalized recommendations. Despite notable advancements, existing approaches still face two critical limitations: first, shallow modality fusion often relies on simple concatenation, failing to exploit rich synergic intra- and inter-modal relationships; second, asymmetric feature treatment—where users are only characterized by interaction IDs while items benefit from rich multimodal content—hinders the learning of a shared semantic space. To address these issues, we propose a C ross-Modal R ecursive A ttention N etwork with Dual Graph E mbedding (CRANE) . To tackle shallow fusion, we design a core Recursive Cross-Modal Attention (RCA) mechanism that iteratively refines modality features based on cross-correlations in a joint latent space, effectively capturing high-order intra- and inter-modal dependencies. For symmetric multimodal learning, we explicitly construct users’ multimodal profiles by aggregating features of their interacted items. Furthermore, CRANE integrates a symmetric dual graph framework—comprising a heterogeneous user–item interaction graph and a homogeneous item–item semantic graph—unified by a self-supervised contrastive learning objective to fuse behavioral and semantic signals. Despite these complex modeling capabilities, CRANE maintains high computational efficiency. Theoretical and empirical analyses confirm its scalability and high practical efficiency, achieving faster convergence on small datasets and superior performance ceilings on large-scale ones. Comprehensive experiments on four public real-world datasets validate an average 5% improvement in key metrics over state-of-the-art baselines. Our code is publicly available at https://github.com/MKC-Lab/CRANE .

Short-video platforms have become a central part of digital content, with users rapidly engaging in various trending topics. Predicting the peak popularity of short-video topics is critical for understanding content dynamics and user behavior. This paper introduces the task of Short-Video Topic Peak Prediction (SVTPP) and proposes both a new dataset and an innovative method. We present the TopicVid dataset, designed to capture the peak trends of short-video topics across multiple platforms. The TopicVid dataset includes 7,701 topics, 58,539 users, and 96,936 videos, with data on views, comments, and shares. This dataset is the first to provide rich semantic information for short-video topic peak prediction, including content details, titles, and user interactions. We propose the Topic Large Graph Model (TLGM), a two-stage framework that integrates heterogeneous graph data with large language models. The TLGM model effectively analyzes the relationships within short-video topics to predict their peak popularity. Experimental results show that our method outperforms existing approaches for predicting short-video topic peaks. Our dataset and code is available at https://github.com/chensh911/TLGM.

Formulating statements that support diverse or controversial stances on specific topics is vital for platforms that enable user expression, reshape political discourse, and drive social critique and information dissemination. With the rise of Large Language Models (LLMs), controllable text generation towards specific stances has become a promising research area with applications in shaping public opinion and commercial marketing. However, existing datasets focus solely on unimodal textual inputs and are primarily designed for stance classification rather than controllable generation. In this work, we introduce the task of stance-driven multimodal response generation, where the goal is to produce a stance-aligned comment given a post on social media that includes both textual and visual information. To support this task, we present StanceGen2024, a new dataset comprising tweet-image/video pairs and stance-annotated user comments from the 2024 U.S. presidential election. The dataset captures rich multimodal interactions and includes fine-grained labels for stance and style, enabling a nuanced study of how multimodal political content shapes stance expression. In addition, we propose SDMG (Stance-Driven Multimodal Generation), a baseline framework that integrates weighted fusion of multimodal features and stance guidance to improve semantic consistency and stance control. The code are available at: https://github.com/blingbq/StanceGen.

Visible-Infrared Person Re-Identification (VI-ReID) plays a crucial role in round-the-clock security surveillance systems, aiming to detect consistent identity recognition across transitions from day to night. A significant challenge in this field is the variation in the appearance of the same identity across visible and infrared modalities, which often leads to coupled noisy labels, referring to both Noisy Annotation (NA) and Noisy Correspondence (NC). Therefore, learning noisy-tolerant and discriminative representations is the primary objective in VI-ReID. However, existing research typically faces two principal limitations: (1) Learning strategies for noisy labeled scenarios usually rely on analyzing the distribution of loss response while ignoring the rich semantic information from neighboring samples. (2) When dealing with identified noisy samples, most previous approaches usually employ filtering strategies to mitigate the impact of noisy samples but fail to consider the valuable information in the noisy samples. To address these challenges, we propose a Propagation based Recycling Contrastive Learning (PRCL) approach. This method utilizes a label propagation strategy to distinguish clean annotations to learn identity-wise semantic information and recycles filtered noisy samples to capture the geometric-wise representation. Thus, even in the presence of noisy labels, the method can help learn robust representations across visible and infrared modalities. Specifically, we design a Noisy-aware Heterogeneous Graph Propagation module, which identifies noisy samples by aggregating the effects of neighboring labels using a graph propagation strategy. In addition, we develop a Cross Modality Recycling Debiased Contrastive Learning algorithm, which leverages the identity-wise information from clean samples and geometry-wise information from noisy samples. This approach utilizes identity-wise and geometric-wise information to mitigate the effect of noisy labels and retain as much valuable information as possible. Extensive experiments on two VI-ReID benchmark datasets demonstrate that our proposed method achieves highly competitive performance.

Explanatory Visual Question Answering (EVQA) is a recently proposed multimodal reasoning task consisting of answering the visual question and generating multimodal explanations for the reasoning processes. Unlike traditional Visual Question Answering (VQA) task that only aims at predicting answers for visual questions, EVQA also aims to generate user-friendly explanations to improve the explainability and credibility of reasoning models. To date, existing methods for VQA and EVQA ignore the prompt in the question and enforce the model to predict the probabilities of all answers. Moreover, existing EVQA methods ignore the complex relationships among question words, visual regions, and explanation tokens. Therefore, in this work, we propose a Logic Integrated Neural Inference Network (LININ) to restrict the range of candidate answers based on first-order-logic (FOL) and capture cross-modal relationships to generate rational explanations. Firstly, we design a FOL-based question analysis program to fetch a small number of candidate answers. Secondly, we utilize a multimodal transformer encoder to extract visual and question features, and conduct the prediction on candidate answers. Finally, we design a multimodal explanation transformer to construct cross-modal relationships and generate rational explanations. Comprehensive experiments on benchmark datasets demonstrate the superiority of LININ compared with the state-of-the-art methods for EVQA.

Graph Neural Networks (GNNs) have recently been utilized to build Collaborative Filtering (CF) models to predict user preferences based on historical user-item interactions. However, there is relatively little understanding of how GNN-based CF models relate to some traditional Network Representation Learning (NRL) approaches. In this paper, we show the equivalence between some state-of-the-art GNN-based CF models and a traditional 1-layer NRL model based on context encoding. Based on a Markov process that trades off two types of distances, we present Markov Graph Diffusion Collaborative Filtering (MGDCF) to generalize some state-of-the-art GNN-based CF models. Instead of considering the GNN as a trainable black box that propagates learnable user/item vertex embeddings, we treat GNNs as an untrainable Markov process that can construct constant context features of vertices for a traditional NRL model that encodes context features with a fully-connected layer. Such simplification can help us to better understand how GNNs benefit CF models. Especially, it helps us realize that ranking losses play crucial roles in GNN-based CF tasks. With our proposed simple yet powerful ranking loss InfoBPR, the NRL model can still perform well without the context features constructed by GNNs. We conduct experiments to perform detailed analysis on MGDCF.

With the rapid development of Internet and the expanding scale of social media, social event classification has attracted increasing attention. The key to social event classification is effectively leveraging the visual and textual semantics for classification. However, most of the existing approaches may suffer from the following limitations: (1) Most of them just simply concatenate the image features and text features to get the multimodal features and ignore the fine-grained semantic relationship between modalities. (2) The majority of them hold the closed-world assumption that all classes in test are already seen in training, while this assumption can be easily broken in real-world applications. In practice, new events on Internet may not belong to any existing/seen class, and therefore cannot be correctly identified by closed-world learning algorithms. To tackle these challenges, we propose an Open-World Social Event Classifier (OWSEC) model in this paper. Firstly, we design a multimodal mask transformer network to capture cross-modal semantic relations and fuse fine-grained multimodal features of social events while masking redundant information. Secondly, we design an open-world classifier and propose a cross-modal event mixture mechanism with a novel open-world classification loss to capture the potential distribution space of the unseen class. Extensive experiments on two public datasets demonstrate the superiority of our proposed OWSEC model for open-world social event classification.

With the growing amount of multimodal data, cross-modal retrieval has attracted more and more attention and become a hot research topic. To date, most of the existing techniques mainly convert multimodal data into a common representation space where similarities in semantics between samples can be easily measured across multiple modalities. However, these approaches may suffer from the following limitations: 1) They overcome the modality gap by introducing loss in the common representation space, which may not be sufficient to eliminate the heterogeneity of various modalities; 2) They treat labels as independent entities and ignore label relationships, which is not conducive to establishing semantic connections across multimodal data; 3) They ignore the non-binary values of label similarity in multi-label scenarios, which may lead to inefficient alignment of representation similarity with label similarity. To tackle these problems, in this article, we propose two models to learn discriminative and modality-invariant representations for cross-modal retrieval. First, the dual generative adversarial networks are built to project multimodal data into a common representation space. Second, to model label relation dependencies and develop inter-dependent classifiers, we employ multi-hop graph neural networks (consisting of Probabilistic GNN and Iterative GNN), where the layer aggregation mechanism is suggested for using propagation information of various hops. Third, we propose a novel soft multi-label contrastive loss for cross-modal retrieval, with the soft positive sampling probability, which can align the representation similarity and the label similarity. Additionally, to adapt to incomplete-modal learning, which can have wider applications, we propose a modal reconstruction mechanism to generate missing features. Extensive experiments on three widely used benchmark datasets, i.e., NUS-WIDE, MIRFlickr, and MS-COCO, show the superiority of our proposed method.

Representation learning of knowledge graphs (KGs) aims to embed both entities and relations as vectors in a continuous low-dimensional space, which has facilitated various applications such as link prediction and entity retrieval. Most existing KG embedding methods focus on modeling the structured fact triples independently and ignore the multi-type relations among triples as well as the variety of data types (e.g., texts and images) associated with entities in KGs, and thus fail to capture the complex and multi-modal information that is inherently inside the entity-relation triples. In this paper, we propose a novel approach for knowledge graph embedding named Contrastive Multi-modal Graph Neural Network (CMGNN), which can encapsulate comprehensive features from multi-modal content descriptions of entities and high-order connectivity structures. Specifically, CMGNN first learns entity embeddings from multi-modal content and then contrasts encodings from multi-relational local neighbors and high-order connectivities to obtain latent representations of entities and relations simultaneously. Experimental results demonstrate that CMGNN can effectively model the multi-modalities and multi-type structures in KGs, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the tasks of link prediction and entity classification.

Personalized micro-video recommendation has attracted a lot of research attention with the growing popularity of micro-video sharing platforms. Many efforts have been made to consider micro-video recommendation as a matching task and shown promising performance, while they only focus on simple features or multi-modal attribute information. Recently, Graph Neural Networks (GNNs) have been employed in many recommendation tasks and achieved impressive success. However, these GNN-based methods may suffer from the following limitations: (1) fail to capture the heterogeneity of nodes in user-video bipartite graphs; (2) ignore the non-local (global) semantic correlation information remained in heterogeneous graphs. In this paper, we present a novel approach, Heterogeneous Graph Contrastive Learning Network (HGCL), for personalized micro-video recommendation. To consider heterogeneity in user-video bipartite graphs, we first introduce a heterogeneous graph encoder network for a high-quality representation learning of users and micro-videos. Specifically, we design a random surfing model to generate node-type specific homogeneous graphs to preserve the heterogeneity. Then we propose a graph contrastive learning framework to achieve representation learning on each node-type specific homogeneous graph by maximizing the mutual information between local patches of a graph and the global representation of the entire graph. Finally, a type-crossing objective function is proposed to jointly integrate the node embeddings from different node types to facilitate high-quality representation learning. Experimental results on real-world datasets in the micro-video recommendation task validate the performance of our method, compared with state-of-the-art baseline algorithms.

Recently, user cold-start recommendations have attracted a lot of attention from industry and academia. In user cold-start recommendation systems, the user attribute information is often used by existing approaches to learn user preferences due to the unavailability of user action data. However, most existing recommendation methods often ignore the sparsity of user attributes in cold-start recommendation systems. To tackle this limitation, this article proposes a novel Inductive Heterogeneous Graph Neural Network (IHGNN) model, which utilizes the relational information in user cold-start recommendation systems to alleviate the sparsity of user attributes. Our model converts new users, items, and associated multimodal information into a Modality-aware Heterogeneous Graph (M-HG) that preserves the rich and heterogeneous relationship information among them. Specifically, to utilize rich and heterogeneous relational information in an M-HG for enriching the sparse attribute information of new users, we design a strategy based on random walk operations to collect associated neighbors of new users by multiple times sampling operation. Then, a well-designed multiple hierarchical attention aggregation model consisting of the intra- and inter-type attention aggregating module is proposed, focusing on useful connected neighbors and neglecting meaningless and noisy connected neighbors to generate high-quality representations for user cold-start recommendations. Experimental results on three real datasets demonstrate that the IHGNN outperforms the state-of-the-art baselines.

Medicine recommendation systems target to recommend a set of medicines given a set of symptoms which play a crucial role in assisting doctors in their daily clinics. Existing approaches are either rule-based or supervised. However, the former heavily relies on expert labeling, which is time-consuming and costly to collect, and the latter suffers from the data sparse problem. To automate medicine recommendation on sparse data, we propose MedRec, which introduces two graphs in modeling: (1) a knowledge graph connecting diseases, medicines, symptoms, and examinations; (2) an attribute graph connecting medicines via shared attributes and molecular structures. These two graphs enhance the connectivity between symptoms and medicines, which thus alleviate the data sparse problem. By learning the interrelationship between diseases, medicines, symptoms and examinations and the inner relationship within medicine, we can acquire unified embedding representations of symptoms and medicines which can be used in medicine recommendation. The experimental results show that the proposed model outperforms state-of-the-art methods. In addition, we find that these two tasks: learning graph representation and medical recommendation can benefit each other.

Micro-video recommendation has attracted extensive research attention with the increasing popularity of micro-video sharing platforms. There exists a substantial amount of excellent efforts made to the micro-video recommendation task. Recently, homogeneous (or heterogeneous) GNN-based approaches utilize graph convolutional operators (or meta-path based similarity measures) to learn meaningful representations for users and micro-videos and show promising performance for the micro-video recommendation task. However, these methods may suffer from the following problems: (1) fail to aggregate information from distant or long-range nodes; (2) ignore the varying intensity of users' preferences for different items in micro-video recommendations; (3) neglect the similarities of multi-modal contents of micro-videos for recommendation tasks. In this paper, we propose a novel Adaptive Anti-Bottleneck Multi-Modal Graph Learning Network for personalized micro-video recommendation. Specifically, we design a collaborative representation learning module and a semantic representation learning module to fully exploit user-video interaction information and the similarities of micro-videos, respectively. Furthermore, we utilize an anti-bottleneck module to automatically learn the importance weights of short-range and long-range neighboring nodes to obtain more expressive representations of users and micro-videos. Finally, to consider the varying intensity of users' preferences for different micro-videos, we design and optimize an adaptive recommendation loss to train our model in an end-to-end manner. We evaluate our method on three real-world datasets and the results demonstrate that the proposed model outperforms the baselines.

With the rapid development of the Internet and the expanding scale of social media, incomplete social event classification has increasingly become a challenging task. The key for incomplete social event classification is to accurately leverage the image-level and text-level information. However, most of the existing approaches may suffer from the following limitations: (1) Most Generative Models use the available features to generate the incomplete modality features for social events classification while ignoring the rich semantic label information. (2) The majority of existing multi-modal methods just simply concatenate the coarse-grained image features and text features of the event to get the multi-modal features to classify social events, which ignores the irrelevant multi-modal features and limits their modeling capabilities. To tackle these challenges, in this paper, we propose an Adaptive Transformer-Based Conditioned Variational Autoencoder Network (AT-CVAE) for incomplete social event classification. In the AT-CVAE, we propose a novel Transformer-based Conditioned Variational Autoencoder to jointly model the textual information, visual information and label information into a unified deep model, which can generate more discriminative latent features and enhance the performance of incomplete social event classification. Furthermore, the Mixture-of-Experts Mechanism is utilized to dynamically acquire the weights of each multi-modal information, which can better filter out the irrelevant multi-modal information and capture the vitally important information. Extensive experiments are conducted on two public event datasets, demonstrating the superior performance of our AT-CVAE method.

As a specific form of story generation, Image-guided Story Ending Generation (IgSEG) is a recently proposed task of generating a story ending for a given multi-sentence story plot and an ending-related image. Unlike existing image captioning tasks or story ending generation tasks, IgSEG aims to generate a factual description that conforms to both the contextual logic and the relevant visual concepts. To date, existing methods for IgSEG ignore the relationships between the multimodal information and do not integrate multimodal features appropriately. Therefore, in this work, we propose Multimodal Memory Transformer (MMT), an end-to-end framework that models and fuses both contextual and visual information to effectively capture the multimodal dependency for IgSEG. Firstly, we extract textual and visual features separately by employing modality-specific large-scale pretrained encoders. Secondly, we utilize the memory-augmented cross-modal attention network to learn cross-modal relationships and conduct the fine-grained feature fusion effectively. Finally, a multimodal transformer decoder constructs attention among multimodal features to learn the story dependency and generates informative, reasonable, and coherent story endings. In experiments, extensive automatic evaluation results and human evaluation results indicate the significant performance boost of our proposed MMT over state-of-the-art methods on two benchmark datasets.

Micro-video recommendation has attracted extensive research attention with the increasing popularity of micro-video sharing platforms. Traditional approaches consider micro-video recommendation as a matching task and ignore the rich relationships among users and micro-videos from various modalities (e.g., visual, acoustic, and textual). Recently, GNN-based approaches show promising performance for the micro-video recommendation task. However, they mainly focus on the homogeneous graph which includes only one type of nodes or relations, and cannot be applied to the heterogeneous graph which consists of users, micro-videos, and related multi-modal information. In this paper, a novel Heterogeneous Hierarchical Feature Aggregation Network (HHFAN) is proposed for personalized micro-video recommendation. Our goal is to explore the highly complicated relationship information among users, micro-videos and related multi-modal information from a modality-aware Heterogeneous Information Graph (M-HIG), and thus generate high-quality user and micro-video embeddings for recommendation. The proposed model consists of two key components: (1) In data structure level, we build a heterogeneous graph and utilize a random walk based sampling strategy to sample neighbors for users and micro-videos. (2) In representation learning level, we design a hierarchical feature aggregation network including the intra- and inter-type feature aggregation networks to better capture the complex structure and rich semantic information in the heterogeneous graph. We evaluate our method on two real-world datasets and the results demonstrate that the proposed model outperforms the baseline methods.

The cross-modal retrieval task has raised continuous attention in recent years with the increasing scale of multi-modal data, which has broad application prospects including multimedia data management and intelligent search engine. Most existing methods mainly project data of different modalities into a common representation space where label information is often exploited to distinguish samples from different semantic categories. However, they typically treat each label as an independent individual and ignore the underlying semantic structure of labels. In this paper, we propose an end-to-end adaptive label-aware graph convolutional network (ALGCN) by designing both the instance representation learning branch and the label representation learning branch, which can obtain modality-invariant and discriminative representations for cross-modal retrieval. Firstly, we construct an instance representation learning branch to transform instances of different modalities into a common representation space. Secondly, we adopt Graph Convolutional Network (GCN) to learn inter-dependent classifiers in the label representation learning branch. In addition, a novel adaptive correlation matrix is proposed to efficiently explore and preserve the semantic structure of labels in a data-driven manner. Together with a robust self-supervision loss for GCN, the GCN model can be supervised to learn an effective and robust correlation matrix for feature propagation. Comprehensive experimental results on three benchmark datasets, NUS-WIDE, MIRFlickr and MS-COCO, demonstrate the superiority of ALGCN, compared with the state-of-the-art methods in cross-modal retrieval.

With the rapid development of social media platforms and the increasing scale of the social media data, the rumor detection task has become vitally important since the authenticity of posts cannot be guaranteed. To date, Many approaches have been proposed to facilitate the rumor detection process by utilizing the multi-task learning mechanism, which aims to improve the performance of rumor detection task by leveraging the useful information in the stance detection task. However, most of the existing approaches suffer from three limitations: (1) only focus on the textual content and ignore the multi-modal information which is key component contained in social media data; (2) ignore the difference of feature space between the stance detection task and rumor detection task, resulting in the unsatisfactory usage of stance information; (3) largely neglect the semantic information hidden in the fine-grained stance labels. Therefore, in this paper, we design a Multi-modal Meta Multi-Task Learning (MM-MTL) framework for social media rumor detection. To make use of multiple modalities, we design a multi-modal post embedding layer which considers both textual and visual content. To overcome the feature-sharing problem of the stance detection task and rumor detection task, we propose a meta knowledge-sharing scheme to share some higher meta network-layers and capture the meta knowledge behind the multi-modal post. To better utilize the semantic information hidden in the fine-grained stance labels, we employ the attention mechanism to estimate the weight of each reply. Extensive experiments on two Twitter benchmark datasets demonstrate that our proposed method achieves state-of-the-art performance.

Cross-modal retrieval has become an active study field with the expanding scale of multimodal data. To date, most existing methods transform multimodal data into a common representation space where semantic similarities between items can be directly measured across different modalities. However, these methods typically suffer from following limitations: 1) They usually attempt to bridge the modality gap by designing losses in the common representation space which may not be sufficient to eliminate potential heterogeneity of different modalities in the common space. 2) They typically treat labels as independent individuals and ignore label relationships which are important for constructing semantic links between multimodal data. In this work, we propose a novel Dual Adversarial Graph Neural Networks (DAGNN) composed of the dual generative adversarial networks and the multi-hop graph neural networks, which learn modality-invariant and discriminative common representations for cross-modal retrieval. Firstly, we construct the dual generative adversarial networks to project multimodal data into a common representation space. Secondly, we leverage the multi-hop graph neural networks, in which a layer aggregation mechanism is proposed to exploit multi-hop propagation information, to capture the label correlation dependency and learn inter-dependent classifiers. Comprehensive experiments conducted on two cross-modal retrieval benchmark datasets, NUS-WIDE and MIRFlickr, indicate the superiority of DAGNN.

The cross-modal image-text retrieval has attracted extensive attention in recent years, which contributes to the development of search engine. Fine-grained features and cross-attention have been widely used in past researches to reach the goal of cross-modal image-text matching. Although cross-related methods have achieved remarkable results, the features must be encoded again in evaluation phase due to the interaction of the two modalities, which is unsuitable for actual scenarios of search engine development. In addition, the aggregated feature does not contain sufficient semantics since it is merely obtained by simple mean pooling. Furthermore, connecting weights of self-attention blocks are target position invariant, which lacks the expected adaptability. To tackle these limitations, in this paper, we propose a novel Global Relation-aware Attention Network (GRAN) for image-text retrieval by designing Global Attention Module (GAM) and Relation-aware Attention Module (RAM) which play an important role in modeling the global feature and the relationships of local fragments. Firstly, we propose Global Attention Module (GAM) followed the fine-grained features to obtain meaningful global feature. Secondly, we use several stacked transformer encoders to further encode features separately. Finally, we propose Relation-aware Attention Module (RAM) to generate a vector which represents the relation information to infer the attention intensity of pairwise fragments. The local features, the global feature, and their relations are considered jointly to conduct an efficient image-text retrieval. Extensive experiments are conducted on the benchmark datasets of Flickr30K and MSCOCO, demonstrating the superiority of our method. On the Flickr30K, compared to the state-of-the-art method TERAN, we improve [email protected](K=1) metric by 5.8% and 4.0 on the image and text retrieval tasks, respectively.

We introduce tf_geometric1, an efficient and friendly library for graph deep learning, which is compatible with both TensorFlow 1.x and 2.x. It provides kernel libraries for building Graph Neural Networks (GNNs) as well as implementations of popular GNNs. The kernel libraries consist of infrastructures for building efficient GNNs, including graph data structures, graph map-reduce framework, graph mini-batch strategy, etc. These infrastructures enable tf_geometric to support single-graph computation, multi-graph computation, graph mini-batch, distributed training, etc.; therefore, tf_geometric can be used for a variety of graph deep learning tasks, such as node classification, link prediction, and graph classification. Based on the kernel libraries, tf_geometric implements a variety of popular GNN models. To facilitate the implementation of GNNs, tf_geometric also provides some other libraries for dataset management, graph sampling, etc. Different from existing popular GNN libraries, tf_geometric provides not only Object-Oriented Programming (OOP) APIs, but also Functional APIs, which enable tf_geometric to handle advanced tasks such as graph meta-learning. The APIs are friendly and suitable for both beginners and experts.

Nowadays, detecting fake news on social media platforms has become a top priority since the widespread dissemination of fake news may mislead readers and have negative effects. To date, many algorithms have been proposed to facilitate the detection of fake news from the hand-crafted feature extraction methods to deep learning approaches. However, these methods may suffer from the following limitations: (1) fail to utilize the multi-modal context information and extract high-order complementary information for each news to enhance the detection of fake news; (2) largely ignore the full hierarchical semantics of textual content to assist in learning a better news representation. To overcome these limitations, this paper proposes a novel hierarchical multi-modal contextual attention network (HMCAN) for fake news detection by jointly modeling the multi-modal context information and the hierarchical semantics of text in a unified deep model. Specifically, we employ BERT and ResNet to learn better representations for text and images, respectively. Then, we feed the obtained representations of images and text into a multi-modal contextual attention network to fuse both inter-modality and intra-modality relationships. Finally, we design a hierarchical encoding network to capture the rich hierarchical semantics for fake news detection. Extensive experiments on three public real datasets demonstrate that our proposed HMCAN achieves state-of-the-art performance.

Among different 3D data representations, point cloud stands out for its efficiency and flexibility. Hence, many researchers have been involved in the point cloud analysis recently. Existing approaches for point cloud segmentation task typically suffer from two limitations: 1) They usually treat different neighbor points as equals which cannot characterize the correlation between the center point and its neighborhoods well. Moreover, different parts may have different local structures for a point cloud, but they just learn a single representation space which is not sufficient and stable. 2) They often capture hierarchical information by heuristic sampling approaches which cannot reveal the spatial relationships of points well to learn global features. To overcome these limitations, we propose a novel hierarchical attentive pooling graph network (HAPGN) which utilizes the gated graph attention network (GGAN) and hierarchical graph pooling module (HiGPool) as building blocks for point cloud segmentation. Specifically, GGAN can highlight not only the importance of different neighbor points but also the importance of different representation spaces to enhance the local feature extraction. HiGPool is a novel pooling module that can capture the spatial layouts of points to learn the hierarchical features adequately. Experimental results on the ShapeNet part dataset and S3DIS dataset show that HAPGN can achieve superior performance over the state-of-the-art segmentation approaches. Furthermore, we also combine our proposed HiGPool with some recent approaches for point cloud classification and achieve better results on the ModelNet40 dataset.

Nowadays, community-based question answering (CQA) systems are popular and have accumulated a large number of questions and answers provided by users. How to accurately match relevant answers for a given question is an essential function in CQA tasks. Recent effective methods utilize word-pair interactions between questions and answers for CQA matching. However, these approaches usually encode questions and answers independently and ignore the fact that they can complement and enhance each other to provide better representations and thus more implicit interactions can be captured. In addition, the visual information, social information and the variable-length problem are usually ignored by most existing approaches. In this paper, a Social-aware Multi-modal Co-attention Convolutional Matching method (SMCACM) is proposed, which models the multi-modal content and social context of questions and answers in a unified framework for CQA matching. A novel co-attention network is proposed to extract complementary information from questions and answers to enhance each other for obtaining better representations, through which our model can capture more implicit interactions between questions and answers. In addition to textual content, our model uses object detection techniques and a meta-path based heterogeneous social representation learning approach to take advantage of the visual content and social context in CQA systems, respectively. Finally, a pooling-based convolutional matching network is designed to infer the matching score based on the complemented questions and answers, which can accept variable-length answers as inputs without padding or cutting. Experimental results on two real-world datasets demonstrate the superior performance of SMCACM compared with other state-of-the-art algorithms.

With the rapid development of social media and the increasing scale of social media data, the rumor detection on social media platforms has become vitally crucial. The key challenges for rumor detection on social media platforms are how to identify rumors deeply entangled with the specific content and how to detect rumors for the emerging social media events without labeled data. Unfortunately, most of the existing approaches can hardly handle these challenges since they tend to learn event-specific features and cannot transfer the learned features to newly emerged events. To tackle the above challenges, we propose a novel Multimodal Disentangled Domain Adaption (MDDA) method which can derive event-invariant features and thus benefit the detection of rumors on emerging social media events. The model consists of two components: the multimodal disentangled representation learning and the unsupervised domain adaptation. The multimodal disentangled representation learning is responsible for disentangling the multimedia posts into the content features and the rumor style features, and removing the content-specific features from post representation. The unsupervised domain adaptation aims to filter out the event-specific features and keep shared rumor style features among events. Based on the final event-invariant rumor style features, we train a robust social media rumor detector that can transfer knowledge from source events to the target events, which can perform well on the newly emerged events. Extensive experiments on two Twitter benchmark datasets demonstrate that our rumor detection model outperforms state-of-the-art methods.

In this article, we focus on fake news detection task and aim to automatically identify the fake news from vast amount of social media posts. To date, many approaches have been proposed to detect fake news, which includes traditional learning methods and deep learning-based models. However, there are three existing challenges: (i) How to represent social media posts effectively, since the post content is various and highly complicated; (ii) how to propose a data-driven method to increase the flexibility of the model to deal with the samples in different contexts and news backgrounds; and (iii) how to fully utilize the additional auxiliary information (the background knowledge and multi-modal information) of posts for better representation learning. To tackle the above challenges, we propose a novel Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks (KMAGCN) to capture the semantic representations by jointly modeling the textual information, knowledge concepts, and visual information into a unified framework for fake news detection. We model posts as graphs and use a knowledge-aware multi-modal adaptive graph learning principal for the effective feature learning. Compared with existing methods, the proposed KMAGCN addresses challenges from three aspects: (1) It models posts as graphs to capture the non-consecutive and long-range semantic relations; (2) it proposes a novel adaptive graph convolutional network to handle the variability of graph data; and (3) it leverages textual information, knowledge concepts and visual information jointly for model learning. We have conducted extensive experiments on three public real-world datasets and superior results demonstrate the effectiveness of KMAGCN compared with other state-of-the-art algorithms.

Knowledge graph embedding (KGE) encodes components of a KG including entities and relations into continuous low vector space. Most existing methods focus on treating entities and relations in triples independently and thus failing to capture the complex and hidden information that is inherently implicit inside the local neighborhood surrounding a triple. In this paper, we present a new approach for knowledge graph completion called GAEAT (Graph Auto-encoder Attention Network Embedding), which can encapsulate both entity and relation features. Specifically, we construct a triple-level auto-encoder by extending graph attention mechanisms to obtain latent representations of entities and relations simultaneously. To justify our proposed model, we evaluate GAEAT on two real-world datasets. The experimental results demonstrate that GAEAT can outperform state-of-the-art KGE models in knowledge graph completion task, which validates the effectiveness of GAEAT. The source code of this paper can be obtained from https://github.com/TomersHan/GAEAT.

Nowadays, with the rapid development of social media, there is a great deal of news produced every day. How to detect fake news automatically from a large of multimedia posts has become very important for people, the government and news recommendation sites. However, most of the existing approaches either extract features from the text of the post which is a single modality or simply concatenate the visual features and textual features of a post to get a multimodal feature and detect fake news. Most of them ignore the background knowledge hidden in the text content of the post which facilitates fake news detection. To address these issues, we propose a novel Knowledge-driven Multimodal Graph Convolutional Network (KMGCN) to model the semantic representations by jointly modeling the textual information, knowledge concepts and visual information into a unified framework for fake news detection. Instead of viewing text content as word sequences normally, we convert them into a graph, which can model non-consecutive phrases for better obtaining the composition of semantics. Besides, we not only convert visual information as nodes of graphs but also retrieve external knowledge from real-world knowledge graph as nodes of graphs to provide complementary semantics information to improve fake news detection. We utilize a well-designed graph convolutional network to extract the semantic representation of these graphs. Extensive experiments on two public real-world datasets illustrate the validation of our approach.

Nowadays, millions of users use community question answering (CQA) systems to share valuable knowledge. An essential function of CQA systems is the accurate matching of answers w.r.t a given question. Recent research exhibits the superior advantages of graph neural networks (GNNs) on modeling content semantics for CQA matching. However, existing GNN-based approaches are insufficient to deal with the multi-modal and redundant properties of CQA systems. In this paper, we propose a multi-modal attentive graph pooling approach (MMAGP) to model the multi-modal content of questions and answers with GNNs in a unified framework, which explores the multi-modal and redundant properties of CQA systems. Our model converts each question/answer into a multi-modal content graph, which can preserve the relational information within multi-modal content. Specifically, to exploit the visual information, we propose an unsupervised meta-path link prediction approach to extract labels from visual content and model them into the multi-modal graph. An attentive graph pooling network is proposed to select vertices in the multi-modal content graph that are significant for the matching adaptively, and generate a pooled graph via aggregating context information for selected vertices. An interaction pooling network is designed to infer the final matching score based on the interactions between the pooled graphs of the input question and answer. Experimental results on two real-world datasets demonstrate the superior performance of MMAGP compared with other state-of-the-art CQA matching models.

Representation learning of medical Knowledge Graph (KG) is an important task and forms the fundamental process for intelligent medical applications such as disease diagnosis and healthcare question answering. Therefore, many embedding models have been proposed to learn vector presentations for entities and relations but they ignore three important properties of medical KG: multi-modal, unbalanced and heterogeneous. Entities in the medical KG can carry unstructured multi-modal content, such as image and text. At the same time, the knowledge graph consists of multiple types of entities and relations, and each entity has various number of neighbors. In this paper, we propose a Multi-modal Multi-Relational Feature Aggregation Network (MMRFAN) for medical knowledge representation learning. To deal with the multi-modal content of the entity, we propose an adversarial feature learning model to map the textual and image information of the entity into the same vector space and learn the multi-modal common representation. To better capture the complex structure and rich semantics, we design a sampling mechanism and aggregate the neighbors with intra and inter-relation attention. We evaluate our model on three knowledge graphs, including FB15k-237, IMDb and Symptoms-in-Chinese with link prediction and node classification tasks. Experimental results show that our approach outperforms state-of-the-art method.

Natural language generation has become a fundamental task in dialogue systems. RNN-based natural response generation methods encode the dialogue context and decode it into a response. However, they tend to generate dull and simple responses. In this article, we propose a novel framework, called KAWA-DRG (Knowledge-aware Attentive Wasserstein Adversarial Dialogue Response Generation) to model conversation-specific external knowledge and the importance variances of dialogue context in a unified adversarial encoder-decoder learning framework. In KAWA-DRG, a co-attention mechanism attends to important parts within and among context utterances with word-utterance-level attention. Prior knowledge is integrated into the conditional Wasserstein auto-encoder for learning the latent variable space. The posterior and prior distribution of latent variables are generated and trained through adversarial learning. We evaluate our model on Switchboard, DailyDialog, In-Car Assistant, and Ubuntu Dialogue Corpus. Experimental results show that KAWA-DRG outperforms the existing methods.

It is critical to comprehensively and efficiently learn user preferences for an effective sequential recommender system. Existing sequential recommendation methods mainly focus on modeling local preference from users’ historical behaviors, which largely ignore the global context information from the heterogeneous information network. This prevents a comprehensive user preference representation. To address these issues, we propose a joint learning approach to incorporate global context with local preferences efficiently. The proposed approach introduces meta-paths from a heterogeneous information network to capture the global context information, and the position-based self-attention mechanism is adopted to model the local preference representation efficiently. Compared with the methods that only consider the local preference, our proposed method takes the advantages of incorporating global context information, which extracts structural features that captures relevant semantics to construct users’ global preference representation for the sequential recommendation. We further adopt a co-attention mechanism to model complex interactions between global context and users’ historical behaviors for better user representations. Quantitative and qualitative experimental evaluations are conducted on nine large-scale Amazon datasets and a multi-modal Zhihu dataset. The promising results demonstrate the effectiveness of the proposed model.

Nowadays, community question answering (CQA) systems have attracted millions of users to share their valuable knowledge. Matching relevant answers for a specific question is a core function of CQA systems. Previous interaction-based matching approaches show promising performance in CQA systems. However, they typically suffer from two limitations: (1) They usually model content as word sequences, which ignores the semantics provided by non-consecutive phrases, long-distance word dependency and visual information. (2) Word-level interactions focus on the distribution of similar words in terms of position, while being agnostic to the semantic-level interactions between questions and answers. To address these limitations, we propose aHierarchical Graph Semantic Pooling Network (HGSPN) to model the hierarchical semantic-level interactions in a unified framework for multi-modal CQA matching. Instead of viewing text content as word sequences, we convert them into graphs, which can model non-consecutive phrases and long-distance word dependency for better obtaining the composition of semantics. In addition, visual content is also modeled into the graphs to provide complementary semantics. A well-designed stacked graph pooling network is proposed to capture the hierarchical semantic-level interactions between questions and answers based on these graphs. A novel convolutional matching network is designed to infer the matching score by integrating the hierarchical semantic-level interaction features. Experimental results on two real-world datasets demonstrate that our model outperforms the state-of-the-art CQA matching models.

Compared with the traditional recommendation system, sequential recommendation holds the ability of capturing the evolution of users' dynamic interests. Many previous studies in sequential recommendation focus on the accuracy of predicting the next item that a user might interact with, while generally ignore providing explanations why the item is recommended to the user. Appropriate explanations are critical to help users adopt the recommended item, and thus improve the transparency and trustworthiness of the recommendation system. In this paper, we propose a novel Explainable Interaction-driven User Modeling (EIUM) algorithm to exploit Knowledge Graph (KG) for constructing an effective and explainable sequential recommender. Qualified semantic paths between specific user-item pair are extracted from KG. Encoding those semantic paths and learning the importance scores for each path provides the path-wise explanation for the recommendation system. Different from traditional item- level sequential modeling methods, we capture the interaction-level user dynamic preferences by modeling the sequential interactions. It is a high- level representation which contains auxiliary semantic information from KG. Furthermore, we adopt a joint learning manner for better representation learning by employing multi-modal fusion, which benefits from the structural constraints in KG and involves three kinds of modalities. Extensive experiments on the large-scale dataset show the better performance of our approach in making sequential recommendations in terms of both accuracy and explainability.

The wide dissemination and misleading effects of online rumors on social media have become a critical issue concerning the public and government. Detecting and regulating social media rumors is important for ensuring users receive truthful information and maintaining social harmony. Most of the existing rumor detection methods focus on inferring clues from media content and social context, which largely ignores the rich knowledge information behind the highly condensed text which is useful for rumor verification. Furthermore, existing rumor detection models underperform on unseen events because they tend to capture lots of event-specific features in seen data which cannot be transferred to newly emerged events. In order to address these issues, we propose a novel Multimodal Knowledge-aware Event Memory Network (MKEMN) which utilizes the Multi-modal Knowledge-aware Network (MKN) and Event Memory Network (EMN) as building blocks for social media rumor detection. Specifically, the MKN learns the multi-modal representation of the post on social media and retrieves external knowledge from real-world knowledge graph to complement the semantic representation of short texts of posts and takes conceptual knowledge as additional evidence to improve rumor detection. The EMN extracts event-invariant features of events and stores them into global memory. Given an event representation, the EMN takes it as a query to retrieve the memory network and output the corresponding features shared among events. With the additional information provided by EMN, our model can learn robust representations of events and consistently perform well on the newly emerged events. Extensive experiments on two Twitter benchmark datasets demonstrate that our rumor detection method achieves much better results than state-of-the-art methods.

Online healthcare services can offer public ubiquitous access to the medical knowledge, especially with the emergence of medical question answering websites, where patients can get in touch with doctors without going to hospital. Explainability and accuracy are two main concerns for medical question answering. However, existing methods mainly focus on accuracy and cannot provide a good explanation for retrieved medical answers. This paper proposes a novelMulti-Modal Knowledge-aware Hierarchical Attention Network (MKHAN) to effectively exploit multi-modal knowledge graph (MKG) for explainable medical question answering. MKHAN can generate path representation by composing the structural, linguistics, and visual information of entities, and infer the underlying rationale of question-answer interactions by leveraging the sequential dependencies within a path from MKG. Furthermore, a novel hierarchical attention network is proposed to discriminate the salience of paths endowing our model with explainability. We build a large-scale multi-modal medical knowledge graph andtwo real-world medical question answering datasets, the experimental results demonstrate the superior performance on our approachcompared with the state-of-the-art methods.

Network representation learning is playing an important role in network analysis due to its effectiveness in a variety of applications. However, most existing network embedding models focus on homogeneous networks and neglect the diverse properties such as different types of network structures and associated multimedia content information. In this article, we learn node representations for multimodal heterogeneous networks, which contain multiple types of nodes and/or links as well as multimodal content such as texts and images. We propose a novel attention-aware collaborative multimodal heterogeneous network embedding method (A 2 CMHNE), where an attention-based collaborative representation learning approach is proposed to promote the collaboration of structure-based embedding and content-based embedding, and generate the robust node representation by introducing an attention mechanism that enables informative embedding integration. In experiments, we compare our model with existing network embedding models on two real-world datasets. Our method leads to dramatic improvements in performance by 5%, and 9% compared with five state-of-the-art embedding methods on one benchmark (M10 Dataset), and on a multi-modal heterogeneous network dataset (WeChat dataset) for node classification, respectively. Experimental results demonstrate the effectiveness of our proposed method on both node classification and link prediction tasks.

Nowadays, community-based question answering (CQA) services have accumulated millions of users to share valuable knowledge. An essential function in CQA tasks is the accurate matching of answers w.r.t given questions. Existing methods usually ignore the redundant, heterogeneous, and multi-modal properties of CQA systems. In this paper, we propose a multi-modal attentive interactive convolutional matching method (MMAICM) to model the multi-modal content and social context jointly for questions and answers in a unified framework for CQA retrieval, which explores the redundant, heterogeneous, and multi-modal properties of CQA systems jointly. A well-designed attention mechanism is proposed to focus on useful word-pair interactions and neglect meaningless and noisy word-pair interactions. Moreover, a multi-modal interaction matrix method and a novel meta-path based network representation approach are proposed to consider the multi-modal content and social context, respectively. The attentive interactive convolutional matching network is proposed to infer the relevance between questions and answers, which can capture both the lexical and the sequential information of the contents. Experiment results on two real-world datasets demonstrate the superior performance of MMAICM compared with other state-of-the-art algorithms.

The sequential recommendation is an important task for online user-oriented services, such as purchasing products, watching videos, and social media consumption. Recent work usually used RNN-based methods to derive an overall embedding of the whole behavior sequence, which fails to discriminate the significance of individual user behaviors and thus decreases the recommendation performance. Besides, RNN-based encoding has fixed size and makes further recommendation application inefficient and inflexible. The online sequential behaviors of a user are generally heterogeneous, polysemous, and dynamically context-dependent. In this paper, we propose a unified Contextual Self-Attention Network (CSAN) to address the three properties. Heterogeneous user behaviors are considered in our model that are projected into a common latent semantic space. Then the output is fed into the feature-wise self-attention network to capture the polysemy of user behaviors. In addition, the forward and backward position encoding matrices are proposed to model dynamic contextual dependency. Through extensive experiments on two real-world datasets, we demonstrate the superior performance of the proposed model compared with other state-of-the-art algorithms.

Taxonomy learning is an important problem and facilitates various applications such as semantic understanding and information retrieval. Previous work for building semantic taxonomies has primarily relied on labor-intensive human contributions or focused on text-based extraction. In this paper, we investigate the problem of automatically learning multimodal taxonomies from the multimedia data on the Web. A systematic framework called Variational Deep Graph Embedding and Clustering (VDGEC) is proposed consisting of two stages as concept graph construction and taxonomy induction via variational deep graph embedding and clustering. VDGEC discovers hierarchical concept relationships by exploiting the semantic textual-visual correspondences and contextual co-occurrences in an unsupervised manner. The unstructured semantics and noisy issues of multimedia documents are carefully addressed by VDGEC for high quality taxonomy induction. We conduct extensive experiments on the real-world datasets. Experimental results demonstrate the effectiveness of the proposed framework, where VDGEC outperforms previous unsupervised approaches by a large gap.

Popularity prediction on social media has attracted extensive attention nowadays due to its widespread applications, such as online marketing and economical trends. In this paper, we describe a solution of our team CASIA-NLPR-MMC for Social Media Prediction (SMP) challenge. This challenge is designed to predict the popularity of social media posts. We present a stacking framework by combining a diverse set of models to predict the popularity of images on Flickr using user-centered, image content and image context features. Several individual models are employed for scoring popularity of an image at earlier stage, and then a stacking model of Support Vector Regression (SVR) is utilized to train a meta model of different individual models trained beforehand. The Spearman's Rho of this Stacking model is 0.88 and the mean absolute error is about 0.75 on our test set. On the official final-released test set, the Spearman's Rho is 0.7927 and mean absolute error is about 1.1783. The results on provided dataset demonstrate the effectiveness of our proposed approach for image popularity prediction.

With a smart phone at hand, it becomes easy now to snap pictures and publish them online with few lines of texts. The GPS coordinates and User-Generated Content (UGC) data embedded in the shared photos provide opportunities to exploit important knowledge to tackle interesting tasks like geographically organizing photos and location visualization. In this work, we propose to organize photos both geographically and semantically, and investigate the problem of location visualization from multiple semantic themes. The novel visualization scheme provides a rich display landscape for geographical exploration from versatile views. A two-level solution is presented, where we first identify the highly photographed places of interest (POI) and discover their focused themes, and then aggregate the lower-level POI themes to generate the higher-level city themes for location visualization. We have conducted experiments on crawled Flickr and Instagram data and exhibited the visualization for the cities of Singapore and Sydney. The experimental results have validated the proposed method and demonstrated the potentials of location visualization from multiple themes.

Newly emerging location-based social media network services (LBSMNS) provide valuable resources to understand users’ behaviors based on their location histories. The location-based behaviors of a user are generally influenced by both user intrinsic interest and the location preference, and moreover are spatial-temporal context dependent. In this article, we propose a spatial-temporal context-aware personalized location recommendation system (STCAPLRS), which offers a particular user a set of location items such as points of interest or venues (e.g., restaurants and shopping malls) within a geospatial range by considering personal interest, local preference, and spatial-temporal context influence. STCAPLRS can make accurate recommendation and facilitate people’s local visiting and new location exploration by exploiting the context information of user behavior, associations between users and location items, and the location and content information of location items. Specifically, STCAPLRS consists of two components: offline modeling and online recommendation. The core module of the offline modeling part is a context-aware regression mixture model that is designed to model the location-based user behaviors in LBSMNS to learn the interest of each individual user, the local preference of each individual location, and the context-aware influence factors. The online recommendation part takes a querying user along with the corresponding querying spatial-temporal context as input and automatically combines the learned interest of the querying user, the local preference of the querying location, and the context-aware influence factor to produce the top- k recommendations. We evaluate the performance of STCAPLRS on two real-world datasets: Dianping and Foursquare. The results demonstrate the superiority of STCAPLRS in recommending location items for users in terms of both effectiveness and efficiency. Moreover, the experimental analysis results also illustrate the excellent interpretability of STCAPLRS.

An ontology hierarchically encodes concepts and concept relationships, and has a variety of applications such as semantic understanding and information retrieval. Previous work for building ontologies has primarily relied on labor-intensive human contributions or focused on text-based extraction. In this paper, we consider the problem of automatically constructing a folksonomy-based visual ontology (FBVO) from the user-generated annotated images. A systematic framework is proposed consisting of three stages as concept discovery, concept relationship extraction, and concept hierarchy construction. The noisy issues of the user-generated tags are carefully addressed to guarantee the quality of derived FBVO. The constructed FBVO finally consists of 139 825 concept nodes and millions of concept relationships by mining more than 2.4 million Flickr images. Experimental evaluations show that the derived FBVO is of high quality and consistent with human perception. We further demonstrate the utility of the derived FBVO in applications of complex visual recognition and exploratory image search.

Nowadays, more and more people are engaged in social media to generate multimedia information, i.e., creating text and photo profiles and posting multimedia messages . Such multimodal social networking activities reveal multiple user attributes such as age, gender, and personal interest. Inferring user attributes is important for user profiling, retrieval , and personalization . Existing work is devoted to inferring user attributes independently and ignores the dependency relations between attributes. In this work, we investigate the problem of relational user attribute inference by exploring the relations between user attributes and extracting both lexical and visual features from online user-generated content. We systematically study six types of user attributes: gender, age, relationship , occupation , interest, and emotional orientation. In view of methodology , we propose a relational latent SVM (LSVM) model to combine a rich set of user features, attribute inference, and attribute relations in a unified framework. In the model, one attribute is selected as the target attribute and others are selected as the auxiliary attributes to assist the target attribute inference. The model infers user attributes and attribute relations simultaneously . Extensive experiments conducted on a collected dataset from Google+ with full attribute annotations demonstrate the effectiveness of the proposed approach in user attribute inference and attribute-based user retrieval.

Most existing approaches on aspect-opinion mining focus on the text domain and cannot be applied to social media where the aspects are essentially multimodal and the opinions depend on the specific aspects. To address the problem of multimodal aspect-opinion mining for entities by leveraging multiple cross-collection sources in social media, in this paper we propose a multimodal aspect-opinion model (mmAOM) considering both user-generated photos and textual documents to simultaneously capture correlations between textual and visual modalities, as well as associations between aspects and opinions . By identifying the aspects and the corresponding opinions related to entities, we apply the mmAOM to entity association visualization and multimodal aspect-opinion retrieval. We have conducted extensive experiments on real-world datasets of entities including Flickr photos, Tripadvisor reviews, and news articles. Qualitative and quantitative evaluation results have validated the effectiveness of the multimodal aspect-opinion mining model, and demonstrated the utility of the derived aspects and opinions from mmAOM in applications of entity association visualization and aspect-opinion retrieval.

Social media is emerging as a new mainstream means of interacting around online media. Social influence mining in social networks is therefore of critical importance in real-world applications such as friend suggestion and photo recommendation. Social media is inherently multimodal, including rich types of user contributed content and social link information. Most of the existing research suffers from two limitations: 1) only utilizing the textual information, and/or 2) only analyzing the generic influence but ignoring the more important topic-level influence. To address these limitations, in this paper we develop a novel Topic-Sensitive Influencer Mining (TSIM) framework in interest-based social media networks. Specifically, we take Flickr as the study platform. People in Flickr interact with each other through images. TSIM aims to find topical influential users and images. The influence estimation is determined with a hypergraph learning approach. In the hypergraph, the vertices represent users and images, and the hyperedges are utilized to capture multi-type relations including visual-textual content relations among images, and social links between users and images. Algorithmwise, TSIM first learns the topic distribution by leveraging user-contributed images, and then infers the influence strength under different topics for each node in the hypergraph. Extensive experiments on a real-world dataset of more than 50 K images and 70 K comment/favorite links from Flickr have demonstrated the effectiveness of our proposed framework. In addition, we also report promising results of friend suggestion and photo recommendation via TSIM on the same dataset.

This article considers the problem of automatically discovering geo-informative attributes for location recognition and exploration. The attributes are expected to be both discriminative and representative, which correspond to certain distinctive visual patterns and associate with semantic interpretations. For our solution, we analyze the attribute at the region level. Each segmented region in the training set is assigned a binary latent variable indicating its discriminative capability. A latent learning framework is proposed for discriminative region detection and geo-informative attribute discovery. Moreover, we use user-generated content to obtain the semantic interpretation for the discovered visual attributes. Discriminative and search-based attribute annotation methods are developed for geo-informative attribute interpretation. The proposed approach is evaluated on one challenging dataset including GoogleStreetView and Flickr photos. Experimental results show that (1) geo-informative attributes are discriminative and useful for location recognition; (2) the discovered semantic interpretation is meaningful and can be exploited for further location exploration.

This paper considers the problem of automatically discovering geo-informative attributes for location recognition and exploration. The attribute is expected to be both discriminative and representative, which corresponds to a distinctive visual pattern and associates with semantic interpretation. For solution, we analyze the attribute at region level. Each segmented region in the training set is assigned a binary latent variable indicating its discriminative capability. A latent learning framework is proposed for discriminative region detection and geo-informative attribute discovery. Moreover, we use user-generated content to obtain the semantic interpretation for the discovered visual attribute. The proposed approach are evaluated on one challenging dataset including GoogleStreetView and Flickr photos. Experimental results show that: (1) geo-informative attribute are discriminative and useful for location recognition; (2) the discovered semantic interpretation is meaningful and can be exploited for further explorations.

The Bag-of-Features (BOF) model is widely used for image classification. Most BOF models incorporate a step of maximum pooling to generate the raw image representation, where salient atoms with maximum response are reserved for final representation. However, recent locality-preserving coding schemes do not account for the saliency characteristic during the process of generating the raw image representations. In this paper, we propose a saliency aware locality-preserving coding scheme by explicitly considering saliency into the dictionary creation and feature coding stages. The novel coding scheme guarantees strong response in the pooling operation and thus contributes to a discriminative image representation. Experiments on three benchmark datasets validate the effectiveness of the proposed method.