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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 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.

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.

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.