Multi-behavior sequential recommendation (MBSR) aims to learn dynamic user preference from historical heterogeneous user interactions for identifying the next item under target behavior (i.e., purchase). Although significant efforts have been devoted to modeling users over observed multi-behavior interaction sequences, user modeling with dynamic behavior-aware multiple interests and elimination of inherent noises within these interactions are still underexplored. This limits user representations' awareness of true preference evolution and further constrains recommendation performance. To address the aforementioned issues, we propose a Multi-Interest Network with Simple Diffusion (MISD) via a combination of multi-interest learning and diffusion generative process for MBSR. Concretely, the dynamic multi-interest network is proposed to generate time-evolving personalized interests from the encoded dual-granularity user sequential patterns, leading to more accurate user preference learning. Additionally, simple diffusion is proposed to model the complex latent preference generation procedures in an iterative denoising manner, thereby alleviating the effect of noisy interactions. Extensive experiments on three real-world datasets demonstrate that MISD consistently outperforms various state-of-the-art recommendation methods under multiple settings (e.g., clean and noisy training).

Learning high-quality vector representations (aka. embeddings) of educational questions lies at the core of knowledge tracing (KT), which defines a task of estimating students’ knowledge states by predicting the probability that they correctly answer questions. Although existing KT efforts have leveraged question information to achieve remarkable improvements, most of them learn question embeddings by following the supervised learning paradigm. In this article, we propose a novel question embedding pre-training method for improving knowledge tracing with self-supervised Bi -graph Co -contrastive learning ( BiCo ). Technically, on the basis of self-supervised learning paradigm, we first select two similar but distinct views (i.e., representing objective and subjective semantic perspectives) as the semantic source of question embeddings. Then, we design a primary task (structure recovery) together with two auxiliary tasks (question difficulty recovery and contrastive learning) to further enhance the representativeness of questions. Finally, extensive experiments conducted on two real-world datasets show BiCo has a higher expressive power that enables KT methods to effectively predict students’ performances.

Community search has become especially important in graph analysis task, which aims to identify latent members of a particular community from a few given nodes. Most of the existing efforts in community search focus on exploring the community structure with a single scale in which the given nodes are located. Despite promising results, the following two insights are often neglected. First, node attributes provide rich and highly related auxiliary information apart from network interactions for characterizing the node properties. Attributes may indicate the community assignment of a node with very few links, which would be difficult to determine from the network structure alone. Second, the multiresolution community affords latent information to depict the hierarchical relation of the network and ensure that one of them is closest to the real one. It is essential for users to understand the underlying structure of the network and explore the community with strong structure and attribute cohesiveness at disparate scales. These aspects motivate us to develop a new community search framework called Multiresolution Local Spectral Attributed Community Search (MLSACS). Specifically, inspired by the local modularity, graph wavelets, and scaling functions, we propose a new Multiresolution Local modularity (MLQ) based on a reconstructed node attribute graph. Furthermore, to detect local communities with cohesive structures and attributes at different scales, a sparse indicator vector is developed based on MLQ by solving a linear programming problem. Extensive experimental results on both synthetic and real-world attributed graphs have demonstrated the detected communities are meaningful and the scale can be changed reasonably.

Quantifying similarities among human diseases is crucial to enhance our understanding of disease biology. Deep learning efforts have been devoted to quantifying disease similarity by integrating multi-view data sources from disparate biological data. However, disease data are often sparse, leading to suboptimal representation of disease given biological entity relationships and labeled disease data are not adequately modeled. In this paper, we propose an effective Synergistic disease Similarity measurement model called SynerSim. SynerSim possesses two key components: a hierarchical biological entity relation perception module to capture disease features from various biological entities, and a disease association capturing module based on signed random walk to model precious disease data. Additionally, SynerSim leverages dual granularity contrastive learning to enhance the representation of diverse biological entities, owing to the ability to enable the synergistic supervision of diseases represented by both homogeneous and heterogeneous information. Experimental results demonstrate that SynerSim achieves outstanding performance in the disease similarity measurement.

Polarized Communities Search (PCS) aims to identify query-dependent communities where positive links predominantly connect nodes within each community, while negative links primarily connect nodes across different communities. Existing solutions primarily focus on modeling network topology, disregarding the crucial factor of node attributes. However, it is non-trivial to incorporate node attributes into PCS. In this paper, we propose a novel method called CO-guided RAndom walk in attributed signed networks (CORA) for PCS. Our approach involves constructing an attribute-based signed network to represent the auxiliary relations between nodes. We introduce a weight assignment mechanism to assess the reliability of edges in the signed network. Then, we design a co-guided random walk scheme that operates on two signed networks to model the connections between network topology and node attributes, thereby enhancing the search outcomes. Finally, we identify polarized communities using the Rayleigh quotient in the signed network. Extensive experiments conducted on three public datasets demonstrate the superior performance of CORA compared to state-of-the-art baselines for polarized communities search.

Heterogeneous information networks (HINs) are widely used in recommender system research due to their ability to model complex auxiliary information beyond historical interactions to alleviate data sparsity problem. Existing HIN-based recommendation studies have achieved great success via performing graph convolution operators between pairs of nodes on predefined metapath induced graphs, but they have the following major limitations. First, existing heterogeneous network construction strategies tend to exploit item attributes while failing to effectively model user relations. In addition, previous HIN-based recommendation models mainly convert heterogeneous graph into homogeneous graphs by defining metapaths ignoring the complicated relation dependency involved on the metapath. To tackle these limitations, we propose a novel recommendation model with two-way metapath encoder for top-N recommendation, which models metapath similarity and sequence relation dependency in HIN to learn node representations. Specifically, our model first learns the initial node representation through a pre-training module, and then identifies potential friends and item relations based on their similarity to construct a unified HIN. We then develop the two-way encoder module with similarity encoder and instance encoder to capture the similarity collaborative signals and relational dependency on different metapaths. Finally, the representations on different meta-paths are aggregated through the attention fusion layer to yield rich representations. Extensive experiments on three real datasets demonstrate the effectiveness of our method.

Image captioning aims to generate a grammatically correct and semantically accurate natural language description of a given image. In order to capture the more complex information contained in the image and expand the relevant external knowledge outside the image to generate better image caption, this paper proposes an end-to-end image captioning framework Flexible Image Captioning via Internal Understanding and External Reasoning (IUER) based on the Transformer model. IUER enhances visual understanding ability and caption reasoning ability to improve image captioning performance. To achieve this goal, we use the semantic features of the core objects detected from the image to guide the visual feature, where the visual feature incorporate the spatial positional relationship information between the objects, then we introduce external knowledge network to obtain information other than the intuitive content from the image. In this way, a high-quality image caption sentence about the given image is generated. Experiments prove that our method is superior to the baseline model and comparable to other state-of-the-art methods.

Semantic learning is a common method for binary vulnerability detection, which can analyze the assembly structure of the binary code. The existing methods such as control flow graphs and labeled semantic flow graphs perform single semantic analysis to determine whether a known vulnerability is hidden in the binary function. However, challenge arises when they perform vulnerability detection cross-platforms. In this paper, we propose a multigranularity semantic-based vulnerability search method to deal with the challenge of cross-platform vulnerability detection, and implement its prototype Taurus. Given the binary function to be searched and vulnerability function, Taurus first extracts their inter-basic-block, inter-function, and inter-module semantic features respectively, and then calculates the similarity distance between them at three levels. Finally, three similarity scores are combined to rank in descending order to obtain the vulnerability detection report. The performance comparison indicates that Taurus outperforms the state-of-the-art approaches in terms of accuracy.

Session-based recommendation (SBR) aims to predict the next item based on anonymous behavior session, which has become increasingly essential in various online services. Prior efforts mainly focus on modeling user preference based on the current session. Although some of them have been proven effective, they fail to address two main challenges in SBR. First, SBR suffers more from the problem of data sparsity due to the very limited user–item interactions, and hence it cannot sufficiently capture complicated item dependency relationships. Second, most of the user-item interaction sequences may be with noisy preference signals due to the uncertainty of user's behaviors, and it is difficult to distill high-quality item for recommendation. In this study, we propose a novel SBR model that exploits Cross-session information for Knowledge-aware Session-based Recommendation (CKSR) to address these two issues. Specifically, cross-session graph and knowledge graph are combined to model a cross-session knowledge graph, based on which a knowledge-aware attention mechanism is performed to capture the complicated transition pattern among interacted items. Each session is then represented as the composition of the global preference and the current interest of that session. Moreover, we leverage the similar sessions for the target session to establish a similar session referral circle and apply an influence coupler to judge the significance of different session referrals. An attentive network is designed to distill session preferences from its unique session referral circle. It dynamically extracts high-quality item from noisy session. Experiments on two benchmark data sets demonstrate that CKSR outperforms the state-of-the-art methods consistently.

Automatically generating a human-like description for a given image is a potential research in artificial intelligence, which has attracted a great of attention recently. Most of the existing attention methods explore the mapping relationships between words in sentence and regions in image, such unpredictable matching manner sometimes causes inharmonious alignments that may reduce the quality of generated captions. In this paper, we make our efforts to reason about more accurate and meaningful captions. We first propose word attention to improve the correctness of visual attention when generating sequential descriptions word-by-word. The special word attention emphasizes on word importance when focusing on different regions of the input image, and makes full use of the internal annotation knowledge to assist the calculation of visual attention. Then, in order to reveal those incomprehensible intentions that cannot be expressed straightforwardly by machines, we inject external knowledge extracted from knowledge graph into the encoder-decoder framework to facilitate meaningful captioning. We validate our model on two freely available captioning benchmarks: Microsoft COCO dataset and Flickr30k dataset. The results demonstrate that our approach achieves state-of-the-art performance and outperforms many of the existing approaches.

Due to the deteriorated quality of feature in the propagation process of the neural network, it may be hard for traditional detector to identify a small object by just utilizing information within one region proposal. To overcome the limitation of the traditional object detector, we proposed a graph based relation mining network, to capture the relation information from labels and images. The semantic relation network is proposed to mine the global semantic relation in labels, and the spatial relation network is proposed to capture the local spatial relation in images. The feature representation is further improved by aggregating the outputs of the two networks. Instead of directly disseminating visual features in the network, the relation mining network explores more advanced feature information. Experiments on the PASCAL VOC and MS COCO datasets demonstrate that key relation information significantly improve the performance of object detection with better ability to detect small objects and reasonable bounding box. The results on COCO dataset demonstrate our method can detect objects robustly, increasing the detection performance of small objects from average precision and average recall by 4.7% and 7.6% respectively in performance relative to Faster R-CNN.

To learn a well-performed image annotation model, a large number of labeled samples are usually required. In this paper, we propose a novel semi-supervised approach based on adaptive weighted fusion for automatic image annotation, which can utilize the labeled data and unlabeled data simultaneously. Firstly, two different classifiers, namely the CNN (convolutional neural network) and the LDA-SVM, are constructed by all the labeled data. These two classifiers are independently represented as different feature views. Then, the most confident data with relevant pseudo-labels are chosen and amalgamated with the whole labeled dataset. After that, the two classifiers are retrained with the new labeled dataset until a stop condition is reached. In each iteration process, the unlabeled samples are labeled by high confidence pseudo-labels that are estimated by an adaptive weighted fusion strategy. Finally, we conduct experiments on two datasets, namely IAPR TC12 and NUS-WIDE, and measure the performance of the model with standard criteria, including precision, recall, F-measure, N+ and mAP. The experimental results show that our approach outperforms many state-of-the-art automatic image annotation approaches.

Recommending valuable contents for microblog users is an important way to improve users' experiences. As high quality descriptors of user semantics, tags have always been used to represent users' interests or attributes. In this work, we propose a microblog recommendation approach via hypergraph random walk tag expansion and user social relation. More specifically, microblogs are considered as hyperedges and terms are taken as hypervertexs for each user, and the weighting strategies for both hyperedges and hypervertexs are established. Random walk is performed on the weighted hypergraph to obtain a number of terms as tags for users. And then the tag similarity matrix and the user-tag matrix can be constructed based on tag probability correlations and weight of each tag. Besides, the significance of user social relation is also considered for recommendation. Moreover, an iterative updating scheme is developed to get the final user-tag matrix for computing the similarities between microblogs and users. Experimental results show that the algorithm is effective in microblog recommendation.

In this paper, we present a microblog recommendation algorithm based on multi-tag correlation. Firstly, a tag retrieval strategy is designed to add tags for unlabeled users, the initial user-tag matrix is then constructed and user-tag weights are set. In order to represent user interests accurately, we fully investigate the associations between the tags. Both inner and outer correlation between tags are defined to conquer the problem of sparsity of user-tag matrix. The user interests can then be decided and microblogs can be recommended to users. Experimental results show that the algorithm is effective for microblog recommendation.

In this paper, we present a semi-supervised clustering method for microblog in which both word-level and microblog (document)-level constraints are automatically generated totally based on statistical information rather than any kind of external knowledge. The key idea is first to explore term correlation data, which investigates both inter and intra correlation of words, and the initial similarity between words can therefore be deduced. And then an iterative method is established to calculate both word similarity and microblog similarity. The mechanism of incorporating dual constraints is presented based on word similarity and microblog similarity. We then formulate short text clustering problem as a non-negative matrix factorization based on dual constraints. Empirical study of two real-world dataset shows the superior performance of our framework in handling noisy and microblogs.

The content and structure of linked information such as sets of web pages or research paper archives are dynamic and keep on changing. Even though different methods are proposed to exploit both the link structure and the content information, no existing approach can effectively deal with this evolution. We propose a novel joint model, called Link-IPLSI, to combine texts and links in a topic modeling framework incrementally. The model takes advantage of a novel link updating technique that can cope with dynamic changes of online document streams in a faster and scalable way. Furthermore, an adaptive asymmetric learning method is adopted to freely control the assignment of weights to terms and citations. Experimental results on two different sources of online information demonstrate the time saving strength of our method and indicate that our model leads to systematic improvements in the quality of classification and link prediction.

This paper presents a framework that actively selects informative documents pairs for semi-supervised document clustering. The semi-supervised document clustering algorithm is a Constrained DBSCAN (Cons-DBSCAN), which incorporates instance-level constraints to guide the clustering process in DBSCAN. By obtaining user feedbacks, our proposed active learning algorithm can get informative instance level constraints to aid clustering process. Experimental results show that Cons-DBSCAN with the proposed active learning approach can provide an appealing clustering performance.