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The Social Internet of Things (SIoT) is presented as a new paradigm of the Internet of Things that solves the problems of network navigability and provides enhanced service discovery and composition. It aims to socialize the IoT devices and allow them to interact just like humans by creating multiple social relationships. In SIoT scenarios, a device can offer multiple services, and different devices can offer the same services with different parameters and factors of interest, which leads to data sparsity and sheer volume of services. However, this sheer volume of available services makes it difficult for devices to navigate and select the ones that best fit their needs or preferences. On the other hand, the heterogeneous nature and dynamic connectivity of SIoT networks raise the cold start problem in service recommendations. Few works explored the integration of trust-aware approaches with latent feature mining in the SIoT recommendation systems. To address these challenges, we proposed a hybrid latent feature mining and trust-aware model to provide a tailored service recommendation in the SIoT environment. Experimental results conducted on a public dataset reveal the increase of service recommendation accuracy and highlight the proposed framework’s effectiveness in meeting recommendation needs within the scope of SIoT environment.

An increasing number of services are being offered online, which leads to great difficulties in selecting appropriate services during mashup development. There have been many service recommendation studies and achieved remarkable results to alleviate the issue of service selection challenge. However, they are limited to suggesting services only for a single round or the next round, and ignore the interactive nature in real-world service recommendation scenarios. As a result, existing methods can’t capture developers’ shifting requirements and obtain the long-term optimal recommendation performance over the whole recommendation process. In this paper, we propose a deep reinforcement learning based interactive service recommendation model (RLISR) to tackle this problem. Specifically, we formulate interaction service recommendation as a multi-round decision-making process, and design a reinforcement learning framework to enable the interactions between mashup developers and service recommender systems. First, we propose a knowledge-graph-based state representation modeling method, wherein we consider both the positive and negative feedbacks of developers. Then, we design an informative reward function from the perspective of boosting recommendation accuracy and reducing the number of recommendation rounds. Finally, we adopt a cascading Q-networks model to cope with the enormous combinational candidate space and learn an optimal recommendation policy. Extensive experiments conducted on a real-world dataset validate the effectiveness of the proposed approach compared to the state-of-the-art service recommendation approaches.

With the rapid development of the web service market, the number of web services shows explosive growth. QoS is an important factor in the recommendation scene; how to accurately recommend a high-quality service for users among the massive number of web services has become a tough problem. Previous methods usually acquired feature interaction information by network structures like DNN to improve the QoS prediction accuracy, but this generates unnecessary computations. Aiming at addressing the above problem, inspired by the multigrained scanning mechanism in a deep forest, we propose a location-aware deep interaction forest approach for web service QoS prediction (LDIF). This approach offers the following innovations: The model fuses the location similarity of users and services as a latent feature representation of them. In addition, we designed a scanning interaction structure (SIS), which obtains multiple local feature combinations from the interaction between user and service features, uses interactive computing to extract feature interaction information, and concatenates the feature interaction information with original features, which aims to enhance the dimension of the features. Equipped with these, we compose a layer-by-layer cascade by using SIS to fuse low- and high-order feature interaction information, and the early-stop mechanism controls the cascade depth to avoid unnecessary computation. The experiments demonstrate that our model outperforms eight other state-of-the-art methods on MAE and RMSE common metrics on real public datasets. [ABSTRACT FROM AUTHOR]

With the popularity and wide adoption of SOA (service‐oriented architecture), a massive amount of Web services emerge on the Internet. It is difficult for users to find the desired services from a large number of services. Thus, service recommendation becomes an effective means to improve the efficiency of using service. Considering that the users' QoS (quality of service) preferences are often unknown or uncertain, the recent QoS‐aware service recommendation methods recommend QoS‐diversified services for users to increase the probability of fulfillment of the service list with a limited number of services on users' potential QoS preferences. However, the existing QoS‐diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the diversified preference requirements are not considered. To this end, this article proposes a service diversity adjustment algorithm, which selects more diversified services outside of the original service recommendation list to replace the services in the present recommendation list to approximate the QoS diversity preference of the active user. In this way, the probability of meeting the user's potential QoS preference requirements is improved. Comprehensive experimental results show that the proposed approach can not only provide personalized and diversified services but also ensure the overall accuracy of the recommendation results. [ABSTRACT FROM AUTHOR]

Abstract With the popularity and wide adoption of SOA (service‐oriented architecture), a massive amount of Web services emerge on the Internet. It is difficult for users to find the desired services from a large number of services. Thus, service recommendation becomes an effective means to improve the efficiency of using service. Considering that the users' QoS (quality of service) preferences are often unknown or uncertain, the recent QoS‐aware service recommendation methods recommend QoS‐diversified services for users to increase the probability of fulfillment of the service list with a limited number of services on users' potential QoS preferences. However, the existing QoS‐diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the diversified preference requirements are not considered. To this end, this article proposes a service diversity adjustment algorithm, which selects more diversified services outside of the original service recommendation list to replace the services in the present recommendation list to approximate the QoS diversity preference of the active user. In this way, the probability of meeting the user's potential QoS preference requirements is improved. Comprehensive experimental results show that the proposed approach can not only provide personalized and diversified services but also ensure the overall accuracy of the recommendation results.

In the context of the rapid advancement of the Industrial Internet and Urban Internet, a crucial trend is emerging in the realization of unified, service-oriented, and componentized encapsulation of IT and OT heterogeneous entities underpinned by a service-oriented architecture. This is pivotal for achieving componentized construction and development of extensive industrial software systems. In addressing the diverse demands of application tasks, the efficient and precise recommendation of service components has emerged as a pivotal concern. Existing recommendation models either focus solely on low-order interactions or emphasize high-order interactions, disregarding the distinction between implicit and explicit aspects within high-order interactions as well as the integration of high-order and low-order interactions. This oversight leads to subpar accuracy in recommendations. Real-world data exhibit intricate structures and nonlinearity. In practical applications, different interaction components exhibit varying predictive capabilities. Therefore, in this paper we propose an EIAFM model that fuses explicit and implicit higher-order feature interactions and introduce an attention mechanism to identify which low-level feature interactions contribute more significantly to the prediction results. This approach leads to increased interpretability, combining both generalization and memory capabilities. Through comprehensive experiments on authentic datasets that align with the characteristics of the Service Recommendation of Industrial Software Components problem, we demonstrate that the EIAFM model excels compared to other cutting-edge models in terms of recommendation effectiveness, with the evaluation metrics for the AUC and log-loss reaching values of 0.9281 and 0.3476, respectively. [ABSTRACT FROM AUTHOR]

Web API is an efficient way for Service-based Software (SBS) development, and mashup is a key technology which merges several web services to deal with the increasing complexity of software requirements and expedite the service-based system development. The efficient service recommendation method is vital for the software development. However, the existing methods often suffer from data sparsity or cold start issues, which should lead to bad effects. Currently, this paper starts with SBS development, and proposes a service recommendation method based on knowledge graph embedding and collaborative filtering (CF) technology. In our model, we first construct a refined knowledge graph using SBS-service co-invocation record and SBS and service related information to mine the potential semantics relationship between SBS and service. Then, we learn the SBS and service entities in the knowledge graph. These heterogeneous entities (SBS and service, etc.) are embedded into the low-dimensional space through the representation learning algorithms of Word2vec and TransR, and the distances between SBS and service vectors are calculated. The input of recommendation model is SBS requirement (target SBS), the similarities functional SBS set is extracted from knowledge graph, which can relieve the cold start problem. Meanwhile, the recommendation model uses CF to recommend service to target SBS. Finally, this paper verifies the effectiveness of method on the real-word dataset. Compared with the several state-of-the-art methods, our method has the best service hit rate and ranking quality. [ABSTRACT FROM AUTHOR]

With the popularity and widespread adoption of the SOA (Service-Oriented Architecture), the number of Web services has increased exponentially. Users tend to use online services for their daily business and software development needs. With the large number of Web service candidates, recommending desirable Web services that meet users' personalized QoS (Quality of Service) requirements becomes a challenging research issue, as the QoS preference is usually difficult to satisfy for users, i.e., the QoS preference is uncertain. To solve this problem, some recent works have aimed to recommend QoS-diversified services to enhance the probability of fulfilling the user's latent QoS preferences. However, the existing QoS-diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the personalized diversity preference requirements are not considered. To this end, this paper proposes to mine a user's diversity preference from the their service invocation history and provides a Web service recommendation algorithm, named PDPP (Personalized Determinantal Point Process), through which a personalized service recommendation list with preferred diversity is generated for the user. Comprehensive experimental results show that the proposed approach can provide personalized and diversified Web services while ensuring the overall accuracy of the recommendation results. [ABSTRACT FROM AUTHOR]

In recent years, the world has witnessed the increased maturity of service-oriented computing. The mashup, as one of the typical service-based applications, aggregates contents from more than one source into a single user interface. Facing the rapid growth of the number of web services, choosing appropriate web services for different mashup sources plays an important issue in mashup development, when, in particular, the new mashup is developed from the scratch. To solve this cold start problem when creating new mashups, we propose a web Service Recommendation approach for Mashup creation based on Graph network, called SRMG. SRMG makes service recommendation based on service characteristics and historical usage. It first leverages Bidirectional Encoder Representations from Transformers, to intelligently discover mashups with similar functionalities based on specifications. Afterward, it employs GraphGAN to obtain representation vectors for mashups and services based on historical usage, and further obtains mashup preferences for each service based on representation vectors. Finally, the new mashup's preference for target services is derived from the preference of existing mashups that are similar to it. The extensive experiments on real datasets from ProgrammableWeb demonstrate that SRMG is superior to the state-of-the-art ones. [ABSTRACT FROM AUTHOR]

Numerous companies create innovative software systems using Web APIs (Application Programming Interfaces). API search engines and API directory services, such as ProgrammableWeb, Rapid API Hub, APIs.guru, and API Harmony, have been developed to facilitate the utilization of various APIs. Unfortunately, most API systems provide only superficial support, with no assistance in obtaining relevant APIs or examples of code usage. To better realize the "FAIR" (Findability, Accessibility, Interoperability, and Reusability) features for the usage of Web APIs, in this study, we developed an API inspection system (referred to as API Prober) to provide a new API directory service with multiple supplemental functionalities. To facilitate the findability and accessibility of APIs, API Prober transforms OAS (OpenAPI Specifications) into a graph structure and automatically annotates the semantic concepts using LDA (Latent Dirichlet Allocation) and WordNet. To enhance interoperability, API Prober also classifies APIs by clustering OAS documents and recommends alternative services to be substituted or merged with the target service. Finally, to support reusability, API Prober makes it possible to retrieve examples of API utilization code in Java by parsing source code in GitHub. The experimental results demonstrate the effectiveness of the API Prober in recommending relevant services and providing usage examples based on real-world client code. This research contributes to providing viable methods to appropriately analyze and cluster Web APIs, and recommend APIs and client code examples. [ABSTRACT FROM AUTHOR]

With the rapid development of the web service market, the number of web services shows explosive growth. QoS is an important factor in the recommendation scene; how to accurately recommend a high-quality service for users among the massive number of web services has become a tough problem. Previous methods usually acquired feature interaction information by network structures like DNN to improve the QoS prediction accuracy, but this generates unnecessary computations. Aiming at addressing the above problem, inspired by the multigrained scanning mechanism in a deep forest, we propose a location-aware deep interaction forest approach for web service QoS prediction (LDIF). This approach offers the following innovations: The model fuses the location similarity of users and services as a latent feature representation of them. In addition, we designed a scanning interaction structure (SIS), which obtains multiple local feature combinations from the interaction between user and service features, uses interactive computing to extract feature interaction information, and concatenates the feature interaction information with original features, which aims to enhance the dimension of the features. Equipped with these, we compose a layer-by-layer cascade by using SIS to fuse low- and high-order feature interaction information, and the early-stop mechanism controls the cascade depth to avoid unnecessary computation. The experiments demonstrate that our model outperforms eight other state-of-the-art methods on MAE and RMSE common metrics on real public datasets.

The network embedding method can map the network nodes to a low-dimensional vector space and ext-ract the feature information of each node effectively. In the field of service recommendation, some studies show that the introduction of network embedding method can effectively alleviate the problem of data sparsity in the recom-mendation process. However, the existing network embedding methods are mostly aimed at a specific structure of the network, and do not cooperate with a variety of relationship networks from the source. Therefore, this paper proposes a service recommendation method based on multi-network hybrid embedding (MNHER), which maps mul-tiple relational networks to the same vector space from vertical and parallel perspectives. Firstly, the social network of users, the shared network of service tags and the user-service heterogeneous information network are constructed. Then, the hybrid embedding method proposed in this paper is used to obtain the embedding vector of users and services in the same vector space. Finally, the service recommendation is made to target users based on the embed-ding vector of users and services. In this paper, the random walk method is further optimized to extract and retain the characteristic information of the original network more effectively. In order to verify the effectiveness of the method proposed in this paper, it is compared with a variety of representative service recommendation methods on three public datasets, and the F-measure values of the service recommendation methods based on single relational network and simply fused multi-relational network are improved by 21% and 15%, respectively. It is proven that the method of multi-network hybrid embedding can effectively coordinate multi-relationship network and improve the quality of service recommendation.

Performing location-based services in a secure and efficient manner that remains a huge challenge for the Internet of Vehicles with numerous privacy and security risks. However, most of the existing privacy protection schemes are based on centralized location servers, which makes them all have a common drawback of a single point of failure and leaking user privacy. The employment of anonymity and cryptography is a well-known solution to the above problem, but its expensive resource consumption and complex cryptographic operations are difficult problems to solve. Based on this, designing a distributed and privacy-secure privacy protection scheme for the Internet of Vehicles is an urgent issue for the smart city. In this paper, we propose a privacy protection scheme for the Internet of Vehicles based on privacy set intersection. Specially, using privacy set intersection and blockchain techniques, we propose two protocols, that is, a dual authentication protocol and a service recommendation protocol. The double authentication protocol not only ensures that both communicating parties are trusted users, but also ensures the reliability of their session keys; while the service recommendation protocol based on pseudorandom function and one-way hash function can well protect the location privacy of users from being leaked. Finally, we theoretically analyze the security that this scheme has, i.e., privacy security, non-repudiation, and anti-man-in-the-middle attack. [ABSTRACT FROM AUTHOR]

In the context of the rapid advancement of the Industrial Internet and Urban Internet, a crucial trend is emerging in the realization of unified, service-oriented, and componentized encapsulation of IT and OT heterogeneous entities underpinned by a service-oriented architecture. This is pivotal for achieving componentized construction and development of extensive industrial software systems. In addressing the diverse demands of application tasks, the efficient and precise recommendation of service components has emerged as a pivotal concern. Existing recommendation models either focus solely on low-order interactions or emphasize high-order interactions, disregarding the distinction between implicit and explicit aspects within high-order interactions as well as the integration of high-order and low-order interactions. This oversight leads to subpar accuracy in recommendations. Real-world data exhibit intricate structures and nonlinearity. In practical applications, different interaction components exhibit varying predictive capabilities. Therefore, in this paper we propose an EIAFM model that fuses explicit and implicit higher-order feature interactions and introduce an attention mechanism to identify which low-level feature interactions contribute more significantly to the prediction results. This approach leads to increased interpretability, combining both generalization and memory capabilities. Through comprehensive experiments on authentic datasets that align with the characteristics of the Service Recommendation of Industrial Software Components problem, we demonstrate that the EIAFM model excels compared to other cutting-edge models in terms of recommendation effectiveness, with the evaluation metrics for the AUC and log-loss reaching values of 0.9281 and 0.3476, respectively.

Abstract With the proliferation of smartphones and an increasing number of services provisioned by clouds, mobile edge computing (MEC) is emerging as a complementary technology of cloud computing. It could provide cloud resources and services by local mobile edge servers, which are normally nearby users. However, a significant challenge is aroused in MEC because of the mobility of users. User trajectory prediction technologies could be used to cope with this issue, which has already played important roles in service recommendation systems with MEC. Unfortunately, little attention and work have been given in service recommendation systems considering users mobility. Thus, in this paper, we propose a mobility-aware personalized service recommendation (MPSR) approach based on user trajectory and quality of service (QoS) predictions. In the proposed method, users trajectory is firstly discovered by a hybrid long-short memory network. Then, given users trajectories, service QoS is predicted, considering the similarity of different users and different edge servers. Finally, services are recommended by a center trajectory strategy through MPSR. Experimental results on a real dataset show that our proposed approach can outperform the traditional recommendation approaches in terms of accuracy in mobile edge computing.

The rapid expansion of the cloud service market is inseparable from its widely acclaimed service model. The rapid increase in the number of cloud services has resulted in the phenomenon of service overload. Service recommendations based on services' function attributes are important because they can help users filter services with specific functions, such as the function of guessing hobbies on shopping websites and daily recommendation functions in the listening app. Nowadays, cloud service market has a large number of services, which have similar functions, but the quality of service (QoS) is very different. Although the recommendation based on services' function attributes satisfies users' basic demands, it ignores the impact of the QoS on the user experience. To further improve users' satisfaction with service recommendations, researchers try to recommend services based on services' non-functional attributes. There is sparsity of the QoS matrix in the real world, which brings obstacles to service recommendation; hence, the prediction of the QoS becomes a solution to overcome this obstacle. Scholars have tried to use collaborative filtering (CF) methods and matrix factorization (MF) methods to predict the QoS, but these methods face two challenges. The first challenge is the sparsity of data; the sparsity makes it difficult for CF to accurately determine whether users are similar, and the gap between the hidden matrices obtained by MF decomposition is large; the second challenge is the cold start of recommendation when new users (or services) participate in the recommendation; its historical record is vacant, making accurately predicting the QoS value be more difficult. To solve the aforementioned problems, this study mainly does the following work: 1) we organized the QoS matrix into a service call record, which contains user characteristic information and current QoS. 2) We proposed a QoS prediction method based on GRU-GAN. 3) We used the time series data for quality predictions and compared some QoS prediction methods, such as CF and MF. The results showed that the prediction results based on GRU-GAN are far superior to other prediction methods under the same data density. We aim to help the engineering community promote their findings, shape the technological revolution, improve multidisciplinary collaborations, and collectively create a better future. [ABSTRACT FROM AUTHOR]

Over two decades, context awareness has been incorporated into recommender systems in order to provide, not only the top-rated items to consumers but also the ones that are suitable to the user context. As a class of context-aware systems, context-aware service recommendation (CASR) aims to bind high-quality services to users, while taking into account their context requirements, including invocation time, location, social profiles, connectivity, and so on. However, current CASR approaches are not scalable with the huge amount of service data (QoS and context information, users reviews and feedbacks). In addition, they lack a rich representation of contextual information, as they adopt a simple matrix view. Moreover, current CASR approaches adopt the traditional user-service relation and they do not allow for multi-relational interactions between users and services in different contexts. To offer a scalable and context-sensitive service recommendation with great analysis and learning capabilities, we provide a rich and multi-relational representation of the CASR knowledge, based on the concept of knowledge graph. The constructed context-aware service knowledge graph (C-SKG) is, then, transformed into a low-dimensional vector space to facilitate its processing. For this purpose, we adopt Dilated Recurrent Neural Networks to propose a context-aware knowledge graph embedding, based on the principles of first-order and subgraph-aware proximity. Finally, a recommendation algorithm is defined to deliver the top-rated services according to the target user's context. Experiments have proved the accuracy and scalability of our solution, compared to state-of-the-art CASR approaches. [ABSTRACT FROM AUTHOR]

With the widespread adoption of service-oriented architectures (SOA), services with the same functionality but the different Quality of Service (QoS) are proliferating, which is challenging the ability of users to build high-quality services. It is often costly for users to evaluate the QoS of all feasible services; therefore, it is necessary to investigate QoS prediction algorithms to help users find services that meet their needs. In this paper, we propose a QoS prediction algorithm called the MFDK model, which is able to fill in historical sparse QoS values by a non-negative matrix decomposition algorithm and predict future QoS values by a deep neural network. In addition, this model uses a Kalman filter algorithm to correct the model prediction values with real-time QoS observations to reduce its prediction error. Through extensive simulation experiments on the WS-DREAM dataset, we analytically validate that the MFDK model has better prediction accuracy compared to the baseline model, and it can maintain good prediction results under different tensor densities and observation densities. We further demonstrate the rationality of our proposed model and its prediction performance through model ablation experiments and parameter tuning experiments. [ABSTRACT FROM AUTHOR]

The era of everythingasaservice led to an explosion of services with similar functionalities on the internet. Quickly obtaining a high-quality service has become a research focus in the field of service recommendation. Studies show that quality of service (QoS) predictions are an effective way to discover services with high quality. However, sparse data and performance fluctuation challenge the accuracy and robustness of QoS prediction. To solve these two challenges, this paper proposes a location-based deep factorization machine model, namely LDFM, by employing information entropy and location projection of users and services. Particularly, our LDFM can be decomposed into three phases: i) extending a raw QoS dataset without introducing additional information, where LDFM projects the existing users (services) in the direction of their position vectors to increase the number of users (services) as well as the number of records that users invoke services; ii) mining a sufficient number of potential features behind the behaviors of users who invoke services, where LDFM employs a factorization machine to extract potential features of breadth with low dimensions (i.e., one and two dimensions) and utilizes deep learning to seek potential depth features with high dimensions; and iii) weighting extracted features within various dimensions, where LDFM employs information entropy to strengthen the positive effects of valid features while reducing the negative impacts generated by biased features. Our experimental results (including t-test analyses) show that our proposed LDFM always performs well under different user-service matrix densities and performs better than existing start-of-the-art methods in terms of the accuracy and robustness of QoS predictions. [ABSTRACT FROM AUTHOR]

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The ubiquity of the Internet of Things (IoT) across diverse applications underscore its pivotal role in seamlessly integrating physical devices to facilitate efficient data collection, analysis, and automation. Consequently, ensuring the Quality of Service (QoS) emerges as a critical imperative. While numerous studies have proposed methodologies focusing on resource optimization, network management, and data processing techniques, several previous approaches to QoS prediction may face constraints in the Internet of Things (IoT) environment, where the dynamic nature of IoT environments demands predictive models capable of adapting to varying conditions. Integrating user and service location data into QoS prediction models is paramount, as it enables personalized service delivery tailored to the user's specific context, thus enhancing the user experience and overall system performance. This paper presents a novel approach to QoS prediction in IoT, harnessing self-attention and collaborative filtering (CF) techniques while incorporating location-based features such as distance from the user to the service. Experimental evaluations on benchmark datasets reveal that our proposed model improves prediction accuracy by up to 7 % compared to existing methods, underscoring its efficacy in enhancing QoS provisioning in IoT environments. [ABSTRACT FROM AUTHOR]

With the rapid development of service-oriented computing, an overwhelming number of web services have been published online. Developers can create mashups that combine one or multiple services to meet complex business requirements. To speed up the mashup development process, recommending suitable services for developers is a vital problem. In this paper, we address the data sparsity and cold-start problems faced in service recommendation, and propose a novel multi-relational graph convolutional network framework (MRGCN) for service recommendation. Specifically, we first construct a multi-relational mashup-service graph with three types of relations, namely composition relation, functional relation, and tagging relation. These three relations are indispensable and complement each other for capturing multi-view information. Then, the three relations in the graph are seamlessly fused with various strategies. Next, graph convolution is performed on the fused multi-relational graph to capture the high-order relational information between mashups and services. Finally, the relevance between mashup requirements and services is predicted based on the learned features on the graph. We conduct extensive experiments on the ProgrammableWeb dataset and demonstrate that our proposed method can outperform state-of-the-art methods in recommending services when only mashup requirements are available. [ABSTRACT FROM AUTHOR]

The rise of high-quality cloud services has made service recommendation a crucial research question. Quality of Service (QoS) is widely adopted to characterize the performance of services invoked by users. For this purpose, the QoS prediction of services constitutes a decisive tool to allow end-users to optimally choose high-quality cloud services aligned with their needs. The fact is that users only consume a few of the broad range of existing services. Thereby, perform a high-accurate service recommendation becomes a challenging task. To tackle the aforementioned challenges, we propose a data sparsity resilient service recommendation approach that aims to predict relevant services in a sustainable manner for end-users. Indeed, our method performs both a QoS prediction of the current time interval using a flexible matrix factorization technique and a QoS prediction of the future time interval using a time series forecasting method based on an AutoRegressive Integrated Moving Average (ARIMA) model. The service recommendation in our approach is based on a couple of criteria ensuring in a lasting way, the appropriateness of the services returned to the active user. The experiments are conducted on a real-world dataset and demonstrate the effectiveness of our method compared to the competing recommendation methods. [ABSTRACT FROM AUTHOR]

With the proliferation of smartphones and an increasing number of services provisioned by clouds, mobile edge computing (MEC) is emerging as a complementary technology of cloud computing. It could provide cloud resources and services by local mobile edge servers, which are normally nearby users. However, a significant challenge is aroused in MEC because of the mobility of users. User trajectory prediction technologies could be used to cope with this issue, which has already played important roles in service recommendation systems with MEC. Unfortunately, little attention and work have been given in service recommendation systems considering users mobility. Thus, in this paper, we propose a mobility-aware personalized service recommendation (MPSR) approach based on user trajectory and quality of service (QoS) predictions. In the proposed method, users trajectory is firstly discovered by a hybrid long-short memory network. Then, given users trajectories, service QoS is predicted, considering the similarity of different users and different edge servers. Finally, services are recommended by a center trajectory strategy through MPSR. Experimental results on a real dataset show that our proposed approach can outperform the traditional recommendation approaches in terms of accuracy in mobile edge computing. [ABSTRACT FROM AUTHOR]

With the development of Internet of Things(IoT), more and more smart devices and services appeared in people's life. These services make life more convenient, while it is difficult for people, especially the elderly, to find them in time, because they are widely distributed in great numbers with small sizes. In order to solve this problem, the service recommendation scheme, which is becoming increasingly important, needs to be used in IoT. However, the traditional web service recommendation schemes are not suitable for the IoT, because they rely more on the historical information rather than the energy state or the user's habit attributes, which are important for IoT. We research the service recommendation scheme, finding that the service recommendation process in IoT is a process of matter flows, which follows the conservation of matter. Therefore, we propose the service recommendation scheme based on tripartite graph with matter diffusion and use the habit feature as a dynamic tag. Based on the balance of matter, we use the positive and negative matter diffusion results on the tripartite as the recommendation results. The results of our evaluation show that the performance of the service recommendation scheme is improved in the precision and recall.

With the advent of Internet of Health (IoH) age, traditional medical or healthy services are gradually migrating to the Web or Internet and have been producing a considerable amount of medical data associated with patients, doctors, medicine, medical infrastructure and so on. Effective fusion and analyses of these IoH data are of positive significances for the scientific disaster diagnosis and medical care services. However, IoH data are often distributed across different departments and contain partial user privacy. Therefore, it is often a challenging task to effectively integrate or mine the sensitive IoH data, during which user privacy is not disclosed. To overcome the above difficulty, we put forward a novel multi-source medical data integration and mining solution for better healthcare services, named PDFM (Privacy-free Data Fusion and Mining). Through PDFM, we can search for similar medical records in a time-efficient and privacy-preserving manner, so as to offer patients with better medical and health services. A group of experiments are enacted and implemented to demonstrate the feasibility of the proposal in this work.

Service recommendation technology is the key to realize the personalization of intelligent services. The recommended services need to meet functional requirements as well as non-functional requirements. Therefore, QoS-based service recommendation came into being. To perform intelligent service recommendations, matching users with convenient services based on QoS becomes an inevitable task. However, most of the service recommendation models are based on user interaction records to predict and recommend, ignoring the service-user correlation and unstable QoS values. In this article, we propose a new service recommendation model. We have performed two-tier filtering calculation on a large number of Web Services, filtering the contextual information of users and services and the instability of services. In the first filtering layer, we take the instability of QoS as an indicator to eliminate invalid services, which significantly reduces the service scale and eliminates the interference of invalid services on the recommendation to a certain extent. Further, we process the contextual information of both users and services in the second filtering layer. Considering the impact of the correlation between the service and the user, we use the geographic location information of the user and the service, and solve the combined features generated by the similarity between the user and the service to filter. Considering the sparsity of the service recommendation environment and the influence of noise generated by useless features, we use a model of factorization machine combined with the attention mechanism for computational processing. It effectively distinguishes the interactive importance of different features. We have conducted many experiments on real dataset, and the results show that our model is better than most baseline model in terms of recommendation performance.

Performing location-based services in a secure and efficient manner that remains a huge challenge for the Internet of Vehicles with numerous privacy and security risks. However, most of the existing privacy protection schemes are based on centralized location servers, which makes them all have a common drawback of a single point of failure and leaking user privacy. The employment of anonymity and cryptography is a well-known solution to the above problem, but its expensive resource consumption and complex cryptographic operations are difficult problems to solve. Based on this, designing a distributed and privacy-secure privacy protection scheme for the Internet of Vehicles is an urgent issue for the smart city. In this paper, we propose a privacy protection scheme for the Internet of Vehicles based on privacy set intersection. Specially, using privacy set intersection and blockchain techniques, we propose two protocols, that is, a dual authentication protocol and a service recommendation protocol. The double authentication protocol not only ensures that both communicating parties are trusted users, but also ensures the reliability of their session keys; while the service recommendation protocol based on pseudorandom function and one-way hash function can well protect the location privacy of users from being leaked. Finally, we theoretically analyze the security that this scheme has, i.e., privacy security, non-repudiation, and anti-man-in-the-middle attack.

With the widespread application of service-oriented architecture (SOA), a flood of similarly functioning services have been deployed online. How to recommend services to users to meet their individual needs becomes the key issue in service recommendation. In recent years, methods based on collaborative filtering (CF) have been widely proposed for service recommendation. However, traditional CF typically exploits only low-dimensional and linear interactions between users and services and is challenged by the problem of data sparsity in the real world. To address these issues, inspired by deep learning, this article proposes a new deep CF model for service recommendation, named location-aware deep CF (LDCF). This model offers the following innovations: 1) the location features are mapped into high-dimensional dense embedding vectors; 2) the multilayer-perceptron (MLP) captures the high-dimensional and nonlinear characteristics; and 3) the similarity adaptive corrector (AC) is first embedded in the output layer to correct the predictive quality of service. Equipped with these, LDCF can not only learn the high-dimensional and nonlinear interactions between users and services but also significantly alleviate the data sparsity problem. Through substantial experiments conducted on a real-world Web service dataset, results indicate that LDCF’s recommendation performance obviously outperforms nine state-of-the-art service recommendation methods. [ABSTRACT FROM AUTHOR]

Choosing appropriate Web services to create new applications plays a significant role for service-based development. The general service recommendation approaches usually prefer to suggest popular and frequently used services. However, nowadays users are becoming increasingly interested in long tail services, which are novel but less concerned. Unfortunately, few of the existing service recommendation methods explicitly takes such long tail services into consideration, leading to the poor diversity and serendipity of recommendation results. Compared with the general service recommendation, the major challenges of recommending long tail services come from: (1) more sparse historical invocation records, and (2) lower quality of textual service description. To address the above challenges, in this paper, we put forward a method for Long tail Service Recommendation based on Cross-view and Contrastive learning (LSRCC). More specifically, we propose a text view that employs recursive neural network, multilayer perceptron and attention mechanism to learn the semantic representations of application and service in a more precise way. In addition, we present an invocation view that employs LightGCN (Light Graph Convolution Network) and local level contrastive learning to capture the implicit connections among applications, services and their tags. Finally, we introduce global level contrastive learning to further integrate the above two views for the long tail service recommendation. The extensive experiments on real-world datasets demonstrate that LSRCC outperforms the state-of-the-art baselines in terms of diversity and tail coverage, with the satisfactory precision and recall, in long tail service recommendation. • A long tail service recommendation method is proposed for application creation. • Contrastive learning is used globally and locally to capture implicit connection. • Double views are used to mitigate the problem of data sparsity and inferiority. • Extensive experiments on the real datasets show its superiority over baselines. [ABSTRACT FROM AUTHOR]

Combined with contexts and SVM,a service recommendation method based on context-embedded support vector machine (SRM-CESVM) was proposed.Firstly,according to the different contexts,the user rating matrix was modified to make it with embedded contexts.Secondly,the rating vectors with embedded contexts were used as service feature vectors to construct training set,meanwhile the dimension of service feature vector were not increased by the introduction of contexts.Thirdly,a separation hyperplane for active user was acquired based on training set using SVM,and then the SVM prediction model was built.Finally,the distances between the feature vector points representing the active users' unused services and the hyperplane were calculated.Considering the distances and the recommendation of similar users,the service list was recommended.The experimental results further demonstrate that the proposed method has better recommendation accuracy under different rating matrix densities and can reduce recommendation time.

Abstract In the age of big data, service recommendation has provided an effective manner to filter valuable information from massive data. Generally, by observing the past service invocation records (Boolean values) distributed across different cloud platforms, a recommender system can infer personalized preferences of a user and recommend him/her new services to gain more profits. However, the historical service invocation records are a kind of private information for users. Therefore, how to protect sensitive user data distributed across multiple cloud platforms is becoming a necessity for successful service recommendations. Additionally, the historical service invocation records often update with time, which call for an efficient and scalable service recommendation method. In view of these challenges, we introduce the multi-probe Simhash technique in information retrieval domain into the recommendation process and further put forward a privacy-preserving recommendation method based on historical service invocation records. At last, we design several experiments on the real-world service quality data in set WS-DREAM. Experimental results show the feasibility of the proposal in terms of producing accurate recommended results while protecting users’ private information contained in historical service invocation records.

In the service network, there exist various objects and rich relations among them. These various objects and rich relations naturally form a heterogeneous information network. Service recommendations can help users to locate their desired services. Previous service recommendation studies mainly aim at homogeneous networks or consider few kinds of relations rather than using the rich heterogeneous information. In this paper, we propose a mashup group preference-based service recommendation method in the heterogeneous information network for mashup creation. First of all, we analyze the historical invocation records between mashups and services and exploit the heterogeneous information to construct diverse meta paths with different semantic meanings. Then, we measure the similarity between the starting object and the ending object from different perspectives and integrate different similarity measures to obtain the hybrid similarity. Next, we introduce group preference to capture the rich interactions among mashups and apply a group preference-based Bayesian personalized ranking algorithm to learn the model. Finally, we recommend a list of personalized ranking services for mashup developers. A series of experiments conducted on a real-world dataset demonstrate the superiority of our proposed approach over other baseline approaches.

Memory-based collaborative filtering (i.e., MCF) is regarded as an effective technique to recommend appropriate services to target users. However, if recommendation data are very sparse in the edge environment, traditional MCF-based recommendation methods probably cannot output any recommended item (or service), i.e., a cold-start recommendation problem occurs. To cope with this cold-start problem, we propose an intelligent recommendation method named Inverse_CF_Rec. Concretely, for a target user, we first search for his/her opposite users (together referred to as “enemy” hereafter); afterward, we infer the possible friends of the target user indirectly according to Social Balance Theory; finally, optimal services are recommended to the target user based on the derived possible friends of the target user. The experiments are conducted on a real-world dataset WS-DREAM to validate the effectiveness and efficiency of our proposal. The experiment results show the advantages of our recommendation method in terms of recommendation accuracy and efficiency.

Quality of Service (QoS) value is usually unknown in service recommendation practice. There are some matrix factorization approaches for predicting the unknown value with a user-service model, which uses a single collaboration with the user's neighbor when looking for different services. However, the QoS value is highly related to the service provider and participants. The services are considered in various collaboration based on different users. By considering the context of services, this paper proposes a QoS prediction model using tensor decomposition based on service collaboration called Service-oriented Tensor (SOT). The prediction approach analyzes service collaboration from other similar services and relevant users by using a three-order tensor. Compared with the traditional model, the experiment results show that the proposed model achieves better prediction accuracy.

Network representation learning has attracted much attention as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network information. In this paper, in the light of user co-tag network and social network, we introduced network representation learning techniques into the learning of user preference, to encode user social relations into a continuous vector space. First, we proposed a hybrid network representation learning approach to effectively utilize users' tagging and social relationships, and then we took it for service recommendation. The experimental results show that, compared with four baselines on two public data sets, the improvement ratio over the baselines is up to 50% in terms of Recall@10 and Precision@10 and the improvement is even more than 90% in terms of NDGG@10 and MRR@10.

In the mobile edge computing environment, there are a large number of mobile edge services which are the carriers of various mobile intelligent applications. So how to recommend the most suitable candidate from such a huge number of available services is an urgent task, especially the recommendation task based on quality-of-service (QoS). In traditional service recommendation, collaborative filtering (CF) has been studied in academia and industry. However, due to the mobility of users and services, there exist several defects that limit the application of the CF-based methods, especially in an edge computing environment. The most important problem is the cold-start. In this paper, we propose an ensemble model which combines the model-based CF and neighborhood-based CF. Our approach has two phases, i.e., global features learning and local features learning. In the first phase, to alleviate the cold-start problem, we propose an improved auto-encoder which deals with sparse inputs by pre-computing an estimate of the missing QoS values and can obtain the effective hidden features by capturing the complex structure of the QoS records. In the second phase, to further improve prediction accuracy, a novel computation method is proposed based on Euclidean distance that aims to address the overestimation problem. We introduce two new concepts, common invocation factor and invocation frequency factor, in similarity computation. Then we propose three prediction models, containing two individual models and one hybrid model. The two individual models are proposed to utilize user similar neighbors and service similar neighbors, and the hybrid model is to utilize all neighbors. The experiments conducted in a real-world dataset show that our models can produce superior prediction results and are not sensitive to parameter settings.

Advancements in information and communication technology, and online web users have given attention to the virtual representation of each user, which is crucial for effective service personalization. Meeting users need and preferences is an ongoing challenge in service personalization. This issue can be addressed through the building of a comprehensive user profile. A user profile is the summary of the user's interests, characteristics, behaviours, and preferences, while user profiling is the system of collecting, organizing and inferring the user profile information. Many reviews on user profiling have been conducted but none focused on the effective profile modeling process. Hence, this article aims to provide a review of the recent state-of-the-art approach to user profiling. These include methods, description, characteristics, and taxonomy of the user profile. The study of the existing user profiling modeling in the aspect of data acquisition, feature extraction, profiling techniques, and profiling approaches (with the identification of their strengths and weaknesses) and the performance measures are also provided. In addition, the research challenges were also discussed with a focus on privacy, datasets, cold start issues, trust issues, and computational complexity. Moreover, the article identified an open research direction that serves as solutions to the identified challenges and motivation for further researchers in advancing user profiling. The findings showed that an effective modeling process enhances the construction of accurate user profile for service personalization.

With the widespread adoption of service-oriented architectures (SOA), services with the same functionality but the different Quality of Service (QoS) are proliferating, which is challenging the ability of users to build high-quality services. It is often costly for users to evaluate the QoS of all feasible services; therefore, it is necessary to investigate QoS prediction algorithms to help users find services that meet their needs. In this paper, we propose a QoS prediction algorithm called the MFDK model, which is able to fill in historical sparse QoS values by a non-negative matrix decomposition algorithm and predict future QoS values by a deep neural network. In addition, this model uses a Kalman filter algorithm to correct the model prediction values with real-time QoS observations to reduce its prediction error. Through extensive simulation experiments on the WS-DREAM dataset, we analytically validate that the MFDK model has better prediction accuracy compared to the baseline model, and it can maintain good prediction results under different tensor densities and observation densities. We further demonstrate the rationality of our proposed model and its prediction performance through model ablation experiments and parameter tuning experiments.

While it is well understood that the emerging Social Internet of Things offers the capability of effectively integrating and managing massive heterogeneous IoT objects, it also presents new challenges for suggesting useful objects with certain service for users due to complex relationships in Social Internet of Things, such as user's object usage pattern and various social relationships among Social Internet of Things objects. In this study, we focus on the problem of service recommendation in Social Internet of Things, which is very important for many applications such as urban computing, smart cities, and health care. We propose a graph-based service recommendation framework by jointly considering social relationships of heterogeneous objects in Social Internet of Things and user's preferences. More exactly, we learn user's preference from his or her object usage events with a latent variable model. Then, we model users, objects, and their relationships with a knowledge graph and regard Social Internet of Things service recommendation as a knowledge graph completion problem, where the "like" property that connects users to services needs to be predicted. To demonstrate the utility of the proposed model, we have built a Social Internet of Things testbed to validate our approach and the experimental results demonstrate its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

As service-oriented computing (SOC) technologies gradually mature, developing service-based systems (such as mashups) has become increasingly popular in recent years. Faced with the rapidly increasing number of Web services, recommending appropriate component services for developers on demand is a vital issue in the development of mashups. In particular, since a new mashup to develop contains no component services, it is a new “user” to a service recommender system. To address this new “user” cold-start problem, we propose a multiplex interaction-oriented service recommendation approach, named MISR, which incorporates three types of interactions between services and mashups into a deep neural network. In this article, we utilize the powerful representation learning abilities provided by deep learning to extract hidden structures and features from various types of interactions between mashups and services. Experiments conducted on a real-world dataset from ProgrammableWeb show that MISR outperforms several state-of-the-art approaches regarding commonly used evaluation metrics. [ABSTRACT FROM AUTHOR]

With the rapid development of service-oriented computing, an overwhelming number of web services have been published online. Developers can create mashups that combine one or multiple services to meet complex business requirements. To speed up the mashup development process, recommending suitable services for developers is a vital problem. In this paper, we address the data sparsity and cold-start problems faced in service recommendation, and propose a novel multi-relational graph convolutional network framework (MRGCN) for service recommendation. Specifically, we first construct a multi-relational mashup-service graph with three types of relations, namely composition relation, functional relation, and tagging relation. These three relations are indispensable and complement each other for capturing multi-view information. Then, the three relations in the graph are seamlessly fused with various strategies. Next, graph convolution is performed on the fused multi-relational graph to capture the high-order relational information between mashups and services. Finally, the relevance between mashup requirements and services is predicted based on the learned features on the graph. We conduct extensive experiments on the ProgrammableWeb dataset and demonstrate that our proposed method can outperform state-of-the-art methods in recommending services when only mashup requirements are available.

In view of the problem of trust relationship in traditional trust-based service recommendation algorithm,and the inaccuracy of service recommendation list obtained by sorting the predicted QoS,a trust expansion and listwise learning-to-rank based service recommendation method (TELSR) was proposed.The probabilistic user similarity computation method was proposed after analyzing the importance of service sorting information,in order to further improve the accuracy of similarity computation.The trust expansion model was presented to solve the sparseness of trust relationship,and then the trusted neighbor set construction algorithm was proposed by combining with the user similarity.Based on the trusted neighbor set,the listwise learning-to-rank algorithm was proposed to train an optimal ranking model.Simulation experiments show that TELSR not only has high recommendation accuracy,but also can resist attacks from malicious users.

Item-based collaborative filtering (i.e., ICF) technique has been widely recruited to make service recommendations in the big data environment. However, the ICF technique only performs well when the data for service recommendation decision-making are stored in a physically centralized manner, while they often fail to recommend appropriate services to a target user in the distributed environment where the involved multiple parties are reluctant to release their data to each other due to privacy concerns. Considering this drawback, we improve the traditional ICF approach by integrating the locality-sensitive hashing (LSH) technique, to realize secure and reliable data publishing. Furthermore, through integrating the published data with little privacy across different platforms, appropriate services are recommended based on our suggested recommendation approach named ICFLSH. At last, simulated experiments are conducted on WS-DREAM data set. Experiment results show that ICFLSH performs better than the competitive approaches in terms of service recommendation accuracy, efficiency, and the capability of privacy-preservation.

In the area of user-generated service, service recommendation and service-linking mode recommendation can help end users select suitable service components for the creation of a new service application. However, existing service recommendation and service-linking mode recommendation methods typically focus on analyzing user-service dualistic relationship and predicting the quality of service values for service composition; the information about the latent interests of the user and the service is largely underexplored, resulting in large computational costs and weak recommendation explanations. To address these issues, we propose an interest-aware service association rule creation (SARC-IA) approach for recommending suitable service components and service linking modes to end users. The proposed solution includes two phases: 1) extracting the latent interests of the user and the service by utilizing the latent Dirichlet allocation model to train the invocation records of users; 2) forming interest subsets by collecting the users with the same interest; and 3) SARC algorithm is employed to generate the service-linking modes in the interest subsets. The performance of the proposed SARC-IA is evaluated via extensive experiments with the ProgrammableWeb data set. The experimental results show that the SARC-IA method outperforms the reference schemes in terms of its support, confidence, recall, precision, and F1-measure results.

With the rapid development of cloud manufacturing (CMfg), quality-of-service (QoS) prediction becomes increasingly important in CMfg service platform because it turns out to be impractical to acquire all service QoS values. In this paper, we present a neighbourhood enhanced matrix factorization approach to predict missing QoS values. We first systematically consider geographical information, sample set diversity computation and platform context to extend basic Pearson Correlation Coefficient (PCC) similarity and extract neighbourhood information. Then, we integrate neighbourhood information into matrix factorization (MF) and make prediction of missing values. Compared with existing methods, the proposed method has the following new features: (1) entropy information is adopted to derive personal weights for different users or services when computing PCC similarity; (2) location information and sample set similarity are considered to enhance PCC similarity; (3) topology information is introduced to address data sparsity issue; (4) neighbourhood information is incorporated with MF to improve prediction accuracy. We conduct an experiment on a real-world dataset which includes web service invocations from 339 service users on 5825 services to verify the feasibility and efficiency of our method. [ABSTRACT FROM AUTHOR]

Managers in electric utilities face the disruption of their conventional business model of selling electricity per kilowatt-hour for invariant prices. However, the forthcoming widespread uptake of sustainable energy technologies – such as rooftop solar, batteries, heat pumps and electric vehicles – by residential customers also represents a chance for local utilities to diversify their service portfolio. To appropriately market these technologies to households, utilities need data on consumers. In this paper, we present a novel data-driven service bundle recommendation model incorporating technologies and tariffs for residential customers based on individual household data. We validate the model in a case study and quantify the utility of sharing different levels of household data. We find substantial synergies of flexible sustainable technologies and time-varying tariffs, leading to higher cost reductions for customers than tariff-switching alone that can be recommended based on easy-to-obtain data. This demonstrates a large potential for energy service bundle marketing by local utilities. The presented Machine Learning recommendation models enable more reliable recommendations than a naive benchmark. Our research thus demonstrates the potential of data-driven utility marketing strategies that focus on service bundling and the integration of customers' energy consumption data. [ABSTRACT FROM AUTHOR]