The exponential growth of mobile applications (apps) have made it increasingly challenging for users to discover apps that align with their interests. To address this challenge, researchers have drawn inspiration from the success of recommender systems in various domains, such as e-commerce, and developed app recommendation methods. However, existing approaches still face significant limitations, including over-smoothing, noise interference in high-dimensional data, semantic loss, and low-quality positive and negative samples, all of which hinder recommendation performance. To tackle the above limitations, this study proposes HCAppRec, a novel app recommendation approach that leverages user-app interaction history and integrates hypergraph neural networks with contrastive learning. HCAppRec first constructs a couple of hypergraphs by exploring the semantic similarities between users and between apps, derived from the user-app interaction data. Then, by integrating hypergraph neural networks with contrastive learning, HCAppRec can not only capture complex high-order relationships among users and apps but also distinguish subtle differences, enhancing the model's robustness and generalization. Extensive experiments on real-world datasets demonstrated that HCAppRec significantly outperforms state-of-the-art methods in comprehensive recommendation performance.

Third-party libraries (TPLs) play a crucial role in software development. Utilizing TPL recommender systems can aid software developers in promptly finding useful TPLs. A number of TPL recommendation approaches have been proposed and among them graph neural network (GNN)-based recommendation is attracting the most attention. However, GNN-based approaches generate node representations through multiple convolutional aggregations, which is prone to introducing noise, resulting in the over-smoothing issue. In addition, due to the high sparsity of labelled data, node representations may be biased in real-world scenarios. To address these issues, this paper presents a TPL recommendation method named Implicit Supervision-assisted Graph Collaborative Filtering (ISGCF). Specifically, it takes the App-TPL interaction relationships as input and employs a popularity-debiased method to generate denoised App and TPL graphs. This reduces the noise introduced during graph convolution and alleviates the over-smoothing issue. It also employs a novel implicitly-supervised loss function to exploit the labelled data to learn enhanced node representations. Extensive experiments on a large-scale real-world dataset demonstrate that ISGCF achieves a significant performance advantage over other state-of-the-art TPL recommendation methods in Recall, NDCG and MAP. The experiments also validate the superiority of ISGCF in mitigating the over-smoothing problem.

Numerous mobile applications (apps) with different functions meet the various needs of users, but users have to spend a lot of time selecting suitable mobile apps. How to select relevant mobile apps for users has become an important issue. Existing studies mainly utilise context, user interest, privacy, security, version, and heterogeneous information to make mobile app recommendations. However, they have at least one of the following limitations: (1) Don't fully integrate the rich heterogeneous information; (2) Don't capture complex structural and semantic information; (3) Don't differentiate the importance of different semantic meta-graphs; (4) Don't consider the influence of different users' rating criteria. Therefore, the predictive performance of these methods is relatively limited. This paper considers the influence of different users' rating criteria for the same app and proposes a weighted meta-graph based mobile app recommendation approach by leveraging matrix factorisation and neural networks. Specifically, the similarity measurement between users and apps considers the difference in users' rating criteria under various semantic meta-graph patterns. The matrix factorisation technology is used to acquire the user's and the app's latent feature matrices. The importance of various semantic meta-graphs is distinguished by exploiting the weight learning. The neural network technology is employed to learn interactions between users and apps, thereby predicting the user's preference for unobserved apps. Experimental results demonstrate the superiority of the proposed approach, the effectiveness of considering differences in users' rating criteria, and the importance of differentiating various semantic meta-graphs.

Recommendation system plays a remarkable role in solving the problem of information overload on the Internet. Existing research demonstrates that a recommended list enclosed with appropriate explanations can enhance the transparency of the system and encourage users to make decisions. Although existing works have achieved effective results, they still suffer from at least one of the following limitations: the work either does not use sentiment information or review information, does not explicitly incorporate review-level sentiment information into the model, is based on review retrieval, and generates explanations in the form of templates or phrases. To tackle the above limitations, this article proposes a REview-level Sentiment information enhanced multiTask learning approach for Explainable Recommendation (RESTER). Specifically, it first considers the user’s review information and analyzes the sentiment polarity contained in the review. Then, the user/item’s identity feature, review feature, and sentiment information are fused into a multitask learning framework by leveraging the implicit correlation between the rating prediction and explanation generation tasks. Comprehensive experiments on datasets in three different domains have shown that the proposed model is superior to all other baselines in both rating prediction and explanation generation tasks.

Mashup technology enables developers to create new applications more readily by combining existing services. As its popularity grows, research on service recommendation for mashup creation has gained increasing attention. Existing recommendation methods have the following limitations: either they are susceptible to data sparsity problems, or they exhibit over-smoothing when aggregating high-order neighbors, resulting in similar and non-specific node feature representations, or they only focus on bipartite graphs and neglect the rich heterogeneous information in the mashup-service ecosystem. To address these issues, we propose a service recommendation method for mashup creation based onlightheterogeneous hypergraphcontrastivelearning (LHGCL). This method first constructs a heterogeneous hypergraph by combining mashup information, service information, the mashup-service interaction data, and their related attribute information. Then, it designs a light hypergraph neural network to capture the high-order relationships between mashups and services. Next, it applies contrastive learning to enhance the representations of mashups and services. Finally, it utilizes the enhanced feature vectors of mashups and services to predict mashup preferences for services. Comprehensive experiments conducted on the real-world ProgrammableWeb dataset demonstrate the superiority of the proposed method and the effectiveness of its key modules.

Third-party APIs (Application Programming Interfaces) are widely used in modern software development nowadays. Inspired by traditional recommender systems, recommending appropriate third-party APIs to developers has attracted a lot of research interest. Existing methods mainly focus on applying techniques such as Matrix Factorization (MF), Factorization Machine (FM), graph neural network (GNN) and hypergraph neural network (HGNN) to solve the recommendation problem. However, some limitations have not been well explored in existing methods: 1) MF and FM based API recommendation methods have difficulties in capturing the high-order interactions between users and APIs and are subject to noisy features. 2) GNN based methods can only be applied to simple graph structures, and suffer from the over-smoothing problem when aggregating high-order neighbor information. 3) HGNN based methods are focused on homogeneous hypergraphs and do not take the extra node attributes into consideration. To tackle the limitations, this paper proposes a third-party API recommendation method based on Heterogeneous Hypergraph Attention Network (HHAN). This method first constructs a heterogeneous hypergraph by exploiting the user-API interaction data and extra API attribute information. It then aggregates the neighbor information on the heterogeneous hypergraph to capture the high-order relationships between APIs and users. Finally, a node - and hyperedge-specific attention mechanism is designed to distinguish the importance of different types of neighbors. Extensive experiments on a real-world dataset crawled from ProgrammableWeb.com demonstrate the effectiveness of the proposed method.

The past decade has witnessed a widespread adoption of IoT devices and services in various applications such as intelligent transportation systems. It is crucial for IoT applications to select high-quality services to boost their reliability and efficiency. The prediction for Quality of Service (QoS) can be used to address this issue. Although a number of QoS prediction approaches have been proposed, their performance may be limited in the IoT environment where context features can significantly impact QoS predictions. Based on the historical QoS records of services, this paper proposes a collaborative QoS prediction approach using self-attention representation and deep factorization machine. The approach first leverages the global and local contextual information of services and users to learn personalized representations. Then, based on the personalized representations, it utilizes a deep factorization machine to make QoS predictions. Extensive experiments conducted on a real-world dataset show that the proposed QoS prediction approach significantly outperforms the other state-of-the-art approaches in terms of prediction accuracy.

Recent studies have shown that graph neural networks (GNNs) are vulnerable against perturbations due to lack of robustness and can therefore be easily fooled. Currently, most works on attacking GNNs are mainly using gradient information to guide the attack and achieve outstanding performance. However, the high complexity of time and space makes them unmanageable for large scale graphs and becomes the major bottleneck that prevents the practical usage. We argue that the main reason is that they have to use the whole graph for attacks, resulting in the increasing time and space complexity as the data scale grows. In this work, we propose an efficient Simplified Gradient-based Attack (SGA) method to bridge this gap. SGA can cause the GNNs to misclassify specific target nodes through a multi-stage attack framework, which needs only a much smaller subgraph. In addition, we present a practical metric named Degree Assortativity Change (DAC) to measure the impacts of adversarial attacks on graph data. We evaluate our attack method on four real-world graph networks by attacking several commonly used GNNs. The experimental results demonstrate that SGA can achieve significant time and memory efficiency improvements while maintaining competitive attack performance compared to state-of-art attack techniques.

Manual evaluation could be time-consuming, unreliable and unreproducible in Chinese-English interpretation. Therefore, it is necessary to develop an automatic scoring system. This paper proposes an accurate automatic scoring model for Chinese-English interpretation via a multi-indicator assessment. From the three dimensions (i.e. keywords, content, and grammar) of the scoring rubrics, three improved attention-based BiLSTM neural models are proposed to learn the text of the transcribed responses. In the feature vectorisation stage, the pre-training model Bert is utilised to vectorise the keywords and content, and a random initialisation is used for the grammar. In addition, the fluency is also taken into account based on the speech speed. The overall holistic score is obtained by fusing the four scores using the random forest regressor. The experimental results demonstrate that the proposed scoring method is effective and can perform as good as the manual scoring.

With the growing number of competing Web services that provide similar functionality, Quality-of-Service (QoS) prediction is becoming increasingly important for various QoS-aware approaches of Web services. Collaborative filtering (CF), which is among the most successful personalized prediction techniques for recommender systems, has been widely applied to Web service QoS prediction. In addition to using conventional CF techniques, a number of studies extend the CF approach by incorporating additional information about services and users, such as location, time, and other contextual information from the service invocations. There are also some studies that address other challenges in QoS prediction, such as adaptability, credibility, privacy preservation, and so on. In this survey, we summarize and analyze the state-of-the-art CF QoS prediction approaches of Web services and discuss their features and differences. We also present several Web service QoS datasets that have been used as benchmarks for evaluating the predition accuracy and outline some possible future research directions.

Blockchain has attracted an increasing amount of researches, and there are lots of refreshing implementations in different fields. Cryptocurrency as its representative implementation, suffers the economic loss due to phishing scams. In our work, accounts and transactions are treated as nodes and edges, thus detection of phishing accounts can be modeled as a node classification problem. Correspondingly, we propose a detecting method based on Graph Convolutional Network and autoencoder to precisely distinguish phishing accounts. Experiments on different large-scale real-world datasets from Ethereum show that our proposed model consistently performs promising results compared with related methods.

Service recommendation is widely used to locate developers' desired services. Previous methods mainly focus on employing collaborative filtering (CF) techniques to recommend services to developers. However, these methods have some problems, such as being sensitive to sparse data and having limited predictive ability to new developers. Generative adversarial network (GAN) can solve the above mentioned problems, since it can learn the data distribution from a limited amount of data and generate a new developer's preference score for a service, even if he/she has not invoked the service. In this paper, we propose a novel GAN based service recommendation method. It first constructs a heterogeneous information network (HIN) by utilizing mashup information, service information and their respective attribute information. Then, it samples meta-paths of different semantic relationships and constructs similarity matrices between mashups and services through meta-paths based similarity measurement. Finally, by leveraging the adversarial training between the discriminator and the generator, the discriminator can effectively guide the generator to generate a preference vector for the developer, thus recommending a list of services for him/her according to his/her given mashup attribute information. Comprehensive experimental results on a real-world dataset demonstrate the superiority of the proposed method.

With the wide adoption of SOA (Service Oriented Architecture), a massive amount of innovative applications emerge in the Internet. One of the popular representations is mashup. It is a new application created by combining different kinds of services. There exist multiple typed objects (e.g., mashup, service, category, tag, provider and description) and relations (e.g., compose and composed by relation between mashups and services, provide and provided by relation between services and providers), which constitute a heterogeneous information network (HIN) naturally. Several approaches already exist for recommending services for users but they are limited to consider only one or two kinds of relations between mashups and services. To apply the rich semantics and enhance recommendation performance, in this paper, we propose a Factorization Machine based service Recommendation approach, called FMRec, on HIN. Specifically, we firstly apply counting-based similarities for meta paths to capture the multiple semantic meanings between mashups and services. And then, we employ matrix factorization to the similarity matrices built by different kinds of meta paths to obtain the mashup latent features and service latent features. Finally, we leverage factorization machine model with a group lasso regularization term to learn the ratings between mashups and services. Comprehensive experiments are conducted on a real-world dataset, indicating that our proposed service recommendation approach significantly improves the quality of the recommendation results compared with existing methods.

Mashups are prevalent Service-Oriented Architecture (SOA) based applications consisting of multiple Web Application Programming Interfaces (APIs) and content. Tags have been extensively used to organize and index mashup services. However, people favor manual tags creation in the past. This approach demands user intervention, which is extremely time-consuming and probes to errors. In this paper we propose a novel Mashup-API-Tag model for automatic mashup tag recommendation. The model simultaneously incorporates the composition relationships between mashups and APIs as well as the annotation relationships between APIs and tags to discover the latent topics. Then the semantic similarity between Web APIs and mashups can be acquired. Subsequently, tags of chosen APIs are recommended to a mashup where the mashup and the APIs are most similar. In addition, we develop a tag filtering algorithm to select the most relevant tags for recommendation. The experimental results on a real world dataset prove that our approach outperforms other methods, including frequency-based methods and the methods that only consider the composition relationships and the annotation relationships separately.

With the development of services computing and cloud computing, number of Web services has increased rapidly, and it becomes quite popular for developers to combine different Web services to build innovative Mash up applications. How to quickly locate desired Web service for developers, however, is still a challenging problem that needs to be addressed. Most existing related work employed keyword-based method to search services and focused on matching users' queries with semantic or syntactic Web service description. They seldom took advantage of relationships between services to improve the performance of service searching. This paper presents a correlation search method by making use of several relationships between Web services, to recommend a user with services that are similar, composable or potentially composable to a target service. One important advantage of this method is that it can guide users to find desired services promptly, and thus improves efficiency of the service discovery process. To mine the different relationships between services, several efficient algorithms are presented. Case studies and experiments show, the above correlation search method not only can recommend Web services to users that are relevant to the users' interest, but also can predict composable relationships between services with high performance.