With the advance of service computing technology, the number of Web APIs has risen dramatically over the Internet. Users tend to use Web APIs to achieve their business needs. However, it is difficult for users to find...
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ISBN:
(数字)9798350349184
ISBN:
(纸本)9798350349191
With the advance of service computing technology, the number of Web APIs has risen dramatically over the Internet. Users tend to use Web APIs to achieve their business needs. However, it is difficult for users to find and select the desirable ones due to the plethora of Web APIs. To address this problem, some collaborative filtering-based Web API recommendation methods have been proposed even though their performance is still far from satisfaction, since they only rely on Mashup-API interactions and feature interactions are not considered in the recommendation model. To further improve the recommendation performance, this paper proposes an interactive Web API recommendation method via exploring both Mashup-API interactions and functional description documents of Mashups and Web APIs. Specifically, LightGCN is employed to derive the node representations for the Mashup-API interaction graph, and BERT model is used for the text representations of functional description documents. Furthermore, the two presentations of both the Mashup and Web API are concatenated as the input of ANFM (Attentional Neural Factorization Machine) model, in which low and high-order feature interactions are fully modeled and the weights of feature interactions are trained by attention mechanism. Solid experiments are conducted over a real-world dataset and the experimental results indicate that the proposed method outperforms the baseline methods.
With the rapid development of service computing technology, how to recommend the desirable Web APIs to developers from the large number of APIs is a challenge. The traditional methods based on collaborative filtering ...
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ISBN:
(数字)9798331509712
ISBN:
(纸本)9798331509729
With the rapid development of service computing technology, how to recommend the desirable Web APIs to developers from the large number of APIs is a challenge. The traditional methods based on collaborative filtering are limited by data sparsity. With the support of multi-dimensional relational feature modeling, graph neural network-based methods are proposed to mitigate data sparsity, but their convolution nature may amplify the noise effect. Therefore, how to simultaneously reduce the impact of sparsity and noisy data has been an open question in the field of API recommendation. To address the problem, this paper proposes a self-supervised learning method based on multi-view hypergraph for Web API recommendation. First, the Mashup-API interaction graph is transformed into a hypergraph, and the hyperedges are used as intermediate hubs to transfer messages between nodes, maintaining the global collaboration effect between Mashup and API nodes. Then, a multi-view strategy is adopted to generate embeddings of Mashups and APIs through information fusion, by which recommendation probability is derived by dot product between the embeddings of Mashups and Web APIs. To train the model parameters effectively, a self-supervised learning method is used to reduce the effect of noisy data to improve the embeddings. Extensive experiments are conducted on a real-world dataset, and the experimental results show that the proposed model outperforms the baselines.
With the development of service computing technology, the number of publicly available Web APIs online has increased dramatically. Developers tend to use Web APIs to implement their software development requirements. ...
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ISBN:
(数字)9798350349184
ISBN:
(纸本)9798350349191
With the development of service computing technology, the number of publicly available Web APIs online has increased dramatically. Developers tend to use Web APIs to implement their software development requirements. However, due to the large number of Web APIs, how to select the appropriate Web API from the huge resource library for Mashup development has become a challenge. To solve this problem, some researchers have proposed Web API recommendation methods based on collaborative filtering or matrix factorization. However, the Web API recommendation performance is still limited due to the reliance on Mashup- API interaction and the ignorance of Mashup and Web API description documents. Moreover, the Mashup-API interaction matrix is extremely sparse, resulting in low accuracy in matrix factorization and collaborative filtering. To further improve the recommendation performance, we propose a novel joint matrix factorization method for the Mashup-API interaction matrix by incorporating the Mashup-Mashup similarity matrix and the API-API similarity matrix. A set of experiments are conducted on a real-world dataset, and the experimental results show that the proposed method outperforms the baselines.
With the advancement of service computing technology, the Internet has witnessed an exponential proliferation of Web APIs. However, the selection of suitable APIs from this vast pool for Mashup creation poses a great ...
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ISBN:
(数字)9798350368550
ISBN:
(纸本)9798350368567
With the advancement of service computing technology, the Internet has witnessed an exponential proliferation of Web APIs. However, the selection of suitable APIs from this vast pool for Mashup creation poses a great challenge for users. Various Web API recommendation methods have been proposed to address this issue, aiming to simplify the complex selection process. Despite these efforts, limited studies have been conducted on complementary function recommendation. In this context, a general complementary Web API recommendation framework based on a learning model, named CoWAR, is designed to recommend complementary Web APIs tailored for Mashup creation, based on the user’s selected Web APIs. Specifically, we propose a data labeling algorithm to generate the labeled dataset based on Mashup-API interactions derived from historical Mashups and Web APIs. Additionally, we employ BERT model to generate representation vectors of Web APIs based on the functionality description documents. Subsequently, we utilize SANFM (Self-Attentional Neural Factorization Machines) to train the complementary Web API recommendation model with the labeled sample dataset based on representation vectors of Web APIs. To the best of our knowledge, this is the first work addressing the complementary function recommendation problem with a learning model. By conducting a set of experiments over a real-world dataset, the effectiveness of the proposed approach is validated. The experimental results demonstrate that the learning model outperforms the traditional machine learning-based models and several deep learning-based models.
RapidAPI serves as a marketplace and management platform that integrates a wide range of Web APIs, offering developers access to a rich pool of API resources. More and more researchers are delving into ways to maximiz...
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With the wide use of Massive Open Online Courses (MOOCs) and online education, the large number and variety of educational resources on the online course open platform make it difficult for users to choose, and the re...
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ISBN:
(数字)9798350349184
ISBN:
(纸本)9798350349191
With the wide use of Massive Open Online Courses (MOOCs) and online education, the large number and variety of educational resources on the online course open platform make it difficult for users to choose, and the requirements for course recommendation algorithms and model performance are getting higher and higher. The existing course recommendation algorithms often ignore the interactive information between n learners and courses and the course descriptions, resulting in poor recommendation performance. To solve the problems above, our work proposes an online course recommendation approach via exploring user interactions and course description documents. Specifically, LightGCN model is used to represent the user-course interaction graph to derive the structural embeddings of users and courses. Moreover, BERT model is used to represent the course description documents to derive the description embeddings of courses. Based on the three embeddings, ANFM (Attention Neural Factorization Machine) model is applied to perform online course recommendation by modeling low-order and high-order feature interactions, and learning the weights of the feature interactions through the attention mechanism. Finally, solid experiments are conducted over a real-world public dataset. Experimental results show that the proposed approach outperforms the baseline methods under NDCG, Recall and Precision.
Automatic detection of Alzheimer's disease (AD) is conducive to intervention in the disease progression. MMSE score prediction can reveal the development of AD. In recent years, some studies have designed multi-ta...
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Accurately predicting survival of esophageal cancer is essential for clinical precision treatment. However, the existing region of interest (ROI) based methods not only require prior medical knowledge to complete the ...
Accurately predicting survival of esophageal cancer is essential for clinical precision treatment. However, the existing region of interest (ROI) based methods not only require prior medical knowledge to complete the delineation of tumor, but may also lead to excessive sensitivity of the model towards ROI. To address these challenges, we design a fully automated CT-guided learning that combines a CNN-Transformer size aware U-Net and a ranked survival prediction network together to automatically predict the survival of patients with esophageal cancer. Specifically, we first incorporate the Transformer with shifted windowing multi-head self-attention mechanism into the base of the encoder in the U-Net to capture the long-range dependency in the 3D CT images. Then, to alleviate the imbalance between the ROI and the background in CT images, we design a size-aware coefficient for the segmentation loss. Finally, we design a ranked pair sorting loss to learn more fully the ranked information hidden in esophageal cancer patients. To validate the effectiveness of our method, we conduct extensive experiments on a dataset containing 759 esophageal cancer samples. The experimental results demonstrate that our proposed method can still achieve the best performance in survival prediction without ROI ground truth.
Isocitrate dehydrogenase (IDH) is a key molecular feature for gliomas, and the prediction of IDH is also an important task for computer-aided diagnosis using magnetic resonance imaging (MRI). To address this changllen...
Isocitrate dehydrogenase (IDH) is a key molecular feature for gliomas, and the prediction of IDH is also an important task for computer-aided diagnosis using magnetic resonance imaging (MRI). To address this changllenge, we introduce a multi-modal MRI-based characteristics inspired network for IDH Genotyping (M 3 CI-Net), which pay more attention to the different characteristics information of different MRI modalities T1, T2, T1ce, Flair. In M 3 CI-Net, a pre-fusion module with multi-channel attention mechanism is used to fuse T1ce and Flair modalities and capture as much as possible luminance and contrast information, and the edge information is obtained from T2 modality by using edge detection module. Finally, the feature information between modalities are fused and input into a CNN-Transformer based encoder structure to extract shared spatial and global information from multi-modal MRI, and the information of multiple scales frome encoder are input into the linear layer for IDH genotype classification after pooling, meanwhile, the CNN based decoder with skip-connection for glioma segmentation works for assisting IDH genotyping. Then, we proposed images’ pre-fusion loss, segmentation loss, IDH genotyping loss, and use uncertainty weight training method to balance the weights of these loss. we evaluate our proposed method on Brats2020, and achieve an acceracy of 0.88, an AUC of 0.94, a specificity of 0.92, a sensitivity of 0.84 in IDH genotyping, which is superior to the state-of-the-art methods.
With the emergence of more and more Web services, finding suitable services becomes a difficult problem. Service link prediction is employed to disclose relationships among services, which facilitates the further deve...
With the emergence of more and more Web services, finding suitable services becomes a difficult problem. Service link prediction is employed to disclose relationships among services, which facilitates the further development of service composition, selection, and recommendation. But the existing link prediction approaches simply utilize the structural features of the service network. In reality, the rich text content in service node description documents also carries latent but fine-grained semantics generated by multifaceted topic-aware factors, yet few efforts are committed to mining them. In this paper, we propose a Web service link prediction method based on a topic-aware heterogeneous graph neural network. Specifically, the method consists of two main layers, including the meta-path intra-decomposition and the meta-path inter-mergence. Meta-path intra-decomposition aims to mine the topic distribution of the meta-paths-based context while capturing fine-grained topic-aware semantics. Meta-path inter-mergence uniquely aggregates topic-aware factors according to the mined distribution and adopts a multifaceted attention mechanism to aggregate different meta-paths, enabling service nodes to generate multifaceted topic-aware embeddings that preserve not only the structure and but also the topic-aware semantics. In addition, a topic prior guidance regularization item is set up for quality assurance of multifaceted topic-aware embedding that depends on global knowledge of the unstructured text content in description documents. Experimental results on real datasets show that our proposed model outperforms other existing baselines methods in the link prediction task, successfully validating the effectiveness of our proposed method.
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