Service Recommendation Approach Research Articles

Long tail service recommendation based on cross-view and contrastive learning

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.

Accurate and Reliable Service Recommendation Based on Bilateral Perception in Multi-Access Edge Computing

Multi-access edge computing (MEC) is an emerging computing paradigm that brings services from the centralized cloud to nearby network edge to improve users’ Quality of Experience (QoE). As massive services with dynamic Quality of Service (QoS) are available in MEC, it becomes challenging for users to find reliable services that satisfy their needs. Therefore, service recommendation technology is urgently needed in MEC. Although existing service recommendation methods work well on recommending popular services that users might be interested in, they fail to recommend services with reliable QoS in the MEC environment. To tackle this issue, an accurate and reliable service recommendation (ARSR) approach based on bilateral perception is proposed, which aims to proactively recommend reliable services by perceiving both users’ service demands and multi-QoS of candidate services. ARSR consists of three main steps. First, a user's service demand is estimated by a context-aware service demand prediction method based on an improved online deep learning model. Then, multiple QoS attributes of candidate services are forecasted by a multidimensional contexts-aware QoS prediction method based on an improved multi-task deep neural network. Finally, the optimal service is recommended to the user based on the predicted QoS. Extensive experiments have been carried out to verify the proposed approach and to prove its performance superiority.

Deep learning framework for multi‐round service bundle recommendation in iterative mashup development

AbstractRecent years have witnessed the rapid development of service‐oriented computing technologies. The boom of Web services increases software developers' selection burden in developing new service‐based systems such as mashups. Timely recommending appropriate component services for developers to build new mashups has become a fundamental problem in service‐oriented software engineering. Existing service recommendation approaches are mainly designed for mashup development in the single‐round scenario. It is hard for them to effectively update recommendation results according to developers' requirements and behaviours (e.g. instant service selection). To address this issue, the authors propose a service bundle recommendation framework based on deep learning, DLISR, which aims to capture the interactions among the target mashup to build, selected (component) services, and the following service to recommend. Moreover, an attention mechanism is employed in DLISR to weigh selected services when recommending a candidate service. The authors also design two separate models for learning interactions from the perspectives of content and invocation history, respectively, and a hybrid model called HISR. Experiments on a real‐world dataset indicate that HISR can outperform several state‐of‐the‐art service recommendation methods to develop new mashups iteratively.

A Hybrid Recommendation Approach for Medical Services That Incorporates Knowledge Graphs

At present, there are a large number of growing medical applications in the application market. It is difficult for users to find satisfactory medical services conveniently and efficiently. The classical collaborative filtering algorithm has some problems, such as cold start, unsatisfactory recommendation results, and so on. This paper proposes a hybrid medical service recommendation approach based on knowledge graph to solve the above problems. This approach introduces the open knowledge graph and establishes the semantic link relationship between the mobile application and the knowledge graph entity. It not only enhances the semantic feature of single application for improving the accuracy of recommendation results, but also realizes the in-depth analysis of the semantic relationship among multiple application entities in the knowledge graph through the TransHR model which can alleviate the cold start problem. Then we design a hybrid recommendation algorithm based on multi-dimensional similarity fusion. This algorithm uses the entropy method to organically integrate the calculation results of multi-dimensional semantic similarity, such as feature vector similarity, entity relation similarity, and user rating similarity. It is convenient and efficient to recommend satisfactory medical application services to target users. Finally, we test and analyze the accuracy and effectiveness of our proposed approach by experiment.

IQSrec: An Efficient and Diversified Skyline Services Recommendation on Incomplete QoS

Recent developments of Internet technologies have accelerated the growth of Web services (<i>e.g.</i>, open APIs). As many services provide similar functionality, service recommendation systems use the Quality of Service (QoS) to help users find optimal services. Space partition attracts significant attention in service recommendation since it improves the diversity of recommendations and accelerates skyline services query. However, existing partition-based service recommendation systems are all implemented on complete QoS. They are not sufficient when some services&#x2019; QoS values are missing or invalid. To this end, we develop a new partition-based service recommendation method on incomplete QoS (named IQSrec) that combines probabilistic skyline query and space partition. The probabilistic skyline query measures top-<inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> skyline services on incomplete QoS. A dimension-based partition is specially designed for splitting the incomplete QoS service space into <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula>-dimensional partitions with the most representative services. The candidate skyline services are chosen from each partition and merged together for probabilistic skyline computation. IQSrec selects the highest skyline probability services in each partition as recommendations. The experiments on the synthetic and real-world datasets show IQSrec can efficiently recommend skyline services on incomplete QoS. IQSrec has higher accuracy and diversity compared to the state-of-the-art service recommendation approaches.

Service recommendation through graph attention network in heterogeneous information networks

Recommending suitable services to users autonomously has become the key to solve the problem of service information overload. Existing recommendation algorithms have some limitations, and discard the side information of the node, or ignore the information of the intermediate node, or omit the feature information of the neighbouring nodes, or not model the pairwise attentive interactions between users and services. To solve the above-mentioned limitations, this paper proposes a service recommendation approach by leveraging the graph attention network (GAT) and co-attention mechanism in heterogeneous information networks (HINs). Specifically, different types of meta-paths are first constructed, and a feature expression is learned for each node in HINs. Then, the feature information of mashups/services is aggregated by the co-attention mechanism. Finally, the multi-layer perceptron (MLP) is applied to recommend suitable services for users. Experiments on a real-world dataset illustrate that the proposed method outperforms other state-of-the-art comparison methods.

Mobility-aware personalized service recommendation in mobile edge computing

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.

Neural and Attentional Factorization Machine-Based Web API Recommendation for Mashup Development

The wide adoption of Service Oriented Architecture (SOA) has driven the creation of a massive amount of applications on the Internet, which includes the popular Mashups composed from multiple existing Web APIs. The availability of a large number of Web APIs with diverse functionalities on the Web makes it difficult for users to find APIs meeting their needs for Mashup development. To relieve this difficulty, recommending Web APIs for Mashup development has become an effective solution. A dozen of service recommendation approaches were proposed based on multi-dimensional features extracted from the service repository over the last couple of years, e.g., similarity based matching methods, matrix factorization based models, and factorization machine based models. Among these existing works, Factorization Machine (FM) based models, in particular the deep learning based FM models, have shown better performance compared with other conventional collaborative filtering techniques. Despite their superiority, the deep learning based FMs still have some strong model assumptions that can harm the recommendation accuracy. For example, it models factorized interactions with the same weight and ignores the non-linear and complex inherent structure in data. In a real-world service recommendation scenario, different predictor variables usually have different predictive power and not all features are predictable for estimating the target. Also, higher-order feature interactions are usually underlain in complex user-service environments. To address these deficiencies, this paper proposes a hybrid factorization machine model with a novel neural network architecture, named NAFM, which integrates a deep neural network to capture the non-linear and complex feature interactions and uses an attention mechanism to capture the varying importance of feature interactions. Comprehensive experiments are conducted on a real-world dataset from ProgrammableWeb. The experimental results show that the proposed approach outperforms the existing state-of-the-art models for service recommendation.

Service recommendation driven by a matrix factorization model and time series forecasting.

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.

Similarity-Maintaining Privacy Preservation and Location-Aware Low-Rank Matrix Factorization for QoS Prediction Based Web Service Recommendation

Web service recommendation plays an important role in building service-oriented systems. QoS-based Web service recommendation has recently gained much attention for providing a promising way to help users find high-quality services. To accurately predict the QoS values of candidate Web services, Web service recommendation systems usually need to collect historical QoS data from users, which will potentially pose a threat to the user's privacy. However, how to simultaneously protect user's privacy and make an accurate prediction has not been well studied. By taking these two aspects into consideration, we propose a novel QoS prediction approach for Web service recommendation in this paper. Specifically, we first design a similarity-maintaining privacy preservation (SPP) strategy, which aims to protect the user's privacy and maintain the utility of user data in the meanwhile. Then, we propose a location-aware low-rank matrix factorization (LLMF) algorithm, which employs the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> L1-norm low-rank matrix factorization to improve the model's robustness, and combines the matrix factorization model with two kinds of location information (continent, longitude and latitude) in the prediction process. Experimental results on two publicly available real-world Web service QoS datasets demonstrate the effectiveness of our privacy-preserving QoS prediction approach.

A service recommendation approach based on trusted user profiles and an enhanced similarity measure

Numerous services issued from the emergence of web technologies drive research on how to provide users with trusted and credible services aligned with their needs. To tackle the service targeting problem, recommender systems have been developed. They are grouped into content-based approaches and collaborative filtering based approaches. Strongly focused on the target user profile, content-based methods are inaccurate when the target user profile is poor. To remedy this, collaborative filtering based methods exploit past experiences from many users. In the literature, they are organized into rating methods and ranking methods. In this paper, we propose a trusted collaborative filtering based approach that combines assets of both rating and ranking methods. Our proposal is built on an enhanced hybrid similarity measure and a novel trustworthiness score that is thereafter used to select trusted and relevant user profiles involved in the prediction process. By employing a customized ranking measure, our method improves the service ranking precision without affecting the rating prediction accuracy. Experiments are conducted on the WS-Dream dataset containing 339 users and real-world Quality of Service values related to 5825 web services. Compared to state-of-the-art collaborative filtering based methods, the obtained results show that our proposal offers the best trade-off in terms of rating prediction accuracy and ranking prediction accuracy.

Mashup Recommendation by Regularizing Matrix Factorization with API Co-Invocations

Mashups are a dominant approach for building data-centric applications, especially mobile applications, in recent years. Since mashups are predominantly based on public data sources and existing APIs, it requires no sophisticated programming knowledge of people to develop mashup applications. The recent prevalence of open APIs and open data sources in the Big Data era has provided new opportunities for mashup development, but at the same time increase the difficulty of selecting the right services for a given mashup task. The API recommendation for mashup differs from traditional service recommendation tasks in lacking the specific QoS information and formal semantic specification of the APIs, which limits the adoption of many existing methods. Although there are a significant number of service recommendation approaches, most of them focus on improving the recommendation accuracy and work pays attention to the diversity of the recommendation results. Another challenge comes from the existence of both explicit and implicit correlations among the different APIs, which are generally neglected by existing recommendation methods. In this paper, we address the above deficiencies of existing approaches by exploring API recommendation for mashups in the reusable composition context, with the goal of helping developers identify the most appropriate APIs for their composition tasks. In particular, we propose a probabilistic matrix factorization approach with implicit correlation regularization to solve the recommendation problem and enhance the recommendation diversity. We conjecture that the co-invocation of APIs in real-world mashups is driven by both the explicit textual similarity and implicit correlations of APIs such as the similarity or the complementary relationship of APIs. We develop a latent variable model to uncover the latent correlations between APIs by analyzing their co-invocation patterns. We further explore the relationships of topics/categories to the proposed approach. We demonstrate the effectiveness of our approach by conducting extensive experiments on a real dataset crawled from ProgrammableWeb.

A graph-based QoS prediction approach for web service recommendation

With the development of the Internet, the recommendation based on Quality of Service(QoS) is proven to be an efficient way to deal with the ever-increasing web services in both industry and academia. However, it is hard to make an accurate recommendation using sparse QoS data, which makes QoS prediction a growing concern in the context of web service recommendation. In this research, a novel Graph-based Matrix Factorization approach(GMF) is proposed for QoS prediction. First, a concept of integrated-graph is put forward to consolidate multi-source information from user–aware context and service-aware context, and to deep mine potential relationships based on QoS matrix. Furthermore, the integrated-graph is divided into several sub-graphs by cutting insignificant edges to reduce noises and strengthen interactions between users and services. Based on the local information of each sub-graph and the global information of integrated-graph, a Gaussian Mixture Model(GMM) of QoS value is built as a fusion method to combine local and global information adaptively and to complete final QoS prediction. The extensive experimental analysis on a publicly available dataset indicate that our graph-based method is both accurate and practical.

WITHDRAWN: Recent web service recommendation methods: A review

Web services have become a substantial part of today’s world they are helping to fulfill users’ needs and are making their life easier by providing the services which the users need as per the expectations. The complications behind the service provisions are kept hidden from the customers and the services are provided in a very simple manner. The web service recommendations are helping the users for choosing the services and the software systems they want to use. The important fact for choosing the best web services is the simple task of utilizing the QoS to recommend the services to the clients. Web services are growing day by day making the paramount data for web services. This is a tedious job for a customer to choose a suitable service. A Contemptible service assurance will lead to unsatisfactory results for the consumers. This paper presents the research and investigation of various web service recommendation approaches.

A time-aware service recommendation based on implicit trust relationships and enhanced user similarities

In recent years, the ever-increasing number of cloud services has led to the service targeting issue. It becomes challenging to provide users with services that fit their needs. Recommender systems address the service targeting problem by helping users to easily retrieve services matching their tastes. Collaborative filtering-based methods are spreadly used recommendation approaches since numerous methods are based on them. However, most of them assume the stationarity of user profiles over time, which is an unrealistic consideration. Indeed, in practice, users’ tastes are dynamic and therefore show a significant variability over time. Time-aware recommender systems effectively integrate the variability of users’ needs to improve the recommendation quality. However, the recommendation credibility is another crucial requirement that is not met by those time-aware recommender systems that moreover badly deal with the data sparsity issue. To address the service targeting problem while meeting requirements on the recommendation reliability and the users’ tastes variability, we propose in this paper a realistic temporal service recommendation approach based on implicit trust relationships inference. Our method integrates the time feature into the recommendation process in order to consider the instantaneity of users’ needs. Our proposal is based on a trusted network that infers implicit trust relationships to ensure the recommendation credibility even in case of data sparsity. Experiments are conducted on real-world service invocations datasets. Our proposal is then compared to existing methods and displays valuable performances in terms of mean absolute error, root mean square error, and normalized discounted cumulative.

Automating Mashup Service Recommendation via Semantic and Structural Features

Increasing physical objects connected to the Internet make it possible for smart things to access all kinds of cloud services. Mashup has been an effective way to the rapid IoT (Internet of Things) application development. It remains a big challenge to bridge the semantic gap between user expectations and application functionality with the development of mashup services. This paper proposes a mashup service recommendation approach via merging semantic features from API descriptions and structural features from the mashup-API network. To validate our approach, large-scale experiments are conducted based on a real-world accessible service repository, ProgrammableWeb. The results show the effectiveness of our proposed approach.

A Deep Neural Network With Multiplex Interactions for Cold-Start Service Recommendation

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.

A Novel Approach for Web Service Recommendation Based on Advanced Trust Relationships

Service recommendation is one of the important means of service selection. Aiming at the problems of ignoring the influence of typical data sources such as service information and interaction logs on the similarity calculation of user preferences and insufficient consideration of dynamic trust relationship in traditional trust-based Web service recommendation methods, a novel approach for Web service recommendation based on advanced trust relationships is presented. After considering the influence of indirect trust paths, the improved calculation about indirect trust degree is proposed. By quantifying the popularity of service, the method of calculating user preference similarity is investigated. Furthermore, the dynamic adjustment mechanism of trust is designed by differentiating the effect of each service recommendation. Integrating these efforts, a service recommendation mechanism is introduced, in which a new service recommendation algorithm is described. Experimental results show that, compared with existing methods, the proposed approach not only has higher accuracy of service recommendation, but also can resist attacks from malicious users more effectively.

Functional and Contextual Attention-Based LSTM for Service Recommendation in Mashup Creation

Service recommendation is a fundamental task in many application environments (e.g., Mashup creation and cloud computing). In the past, various methods have been proposed to facilitate the service selection process based on the original functional descriptions. However, the mined features from the descriptions are usually too sparse for training a well-performed model. In addition, most methods neglect to differentiate the weights of various features, while words included in descriptions usually exhibit different intentions (e.g., functional or non-functional). To address these challenges, in this paper we propose a text expansion and deep model-based approach for service recommendation. Specifically, we first expand the description of services at sentence level based on a novel probabilistic topic model that learns topics of words, sentences and descriptions in a stratified fashion. The expansion process can bridge the vocabulary gap between services and user queries with the collective semantic similarity of sentences and descriptions. Then, we propose a Long Short-Term Memory-based model to recommend services with two attention mechanisms - a functional attention mechanism that takes tags as functional prior to mine the function-related features of services and Mashups, and a contextual attention mechanism that considers Mashup requirements as application scenario to help select the most appropriate services. We evaluate the proposed approach on a real-world dataset and the results show it has an improvement of 34 percent in F-measure over the basic LSTM model.

A personalized clustering-based and reliable trust-aware QoS prediction approach for cloud service recommendation in cloud manufacturing

With the growing popularity of cloud manufacturing (CMfg), an increasing number of functionally equivalent cloud services are available on the Internet, which makes cloud service recommendation an important topic recently. Since the quality-of-service (QoS) varies largely in cloud environment, many Collaborative Filtering (CF) approaches are recently proposed to predict the uncertain QoS by utilizing the historical QoS records of similar users. In addition to the data sparsity problem, however, most existing approaches also ignore the task similarity among different users, while dissimilar tasks requested by similar users may lead to quite different QoS prediction results, and thus cannot achieve higher prediction accuracy and recommendation quality. To address this problem, we develop a novel clustering-based and trust-aware approach. Firstly, a set of similar users can be identified from the view of task similarity using clustering-based algorithm, where task similarity can be computed by incorporating both explicit textual information and rating information as well as implicit context information. Secondly, considering the fact that the QoS values may be contributed by unreliable users, we design a trust-aware CF approach by merging local and global trust values, to reconstruct trust network of the clustered users. Finally, we combine the clustering-based algorithm and trust-aware approach to make a more personalized QoS prediction and reliable cloud service recommendation for the active user in CMfg. Experimental results on two real-world data sets demonstrate the effectiveness of the proposed approach compared with other state-of-the-art approaches.