The Experts below are selected from a list of 4440 Experts worldwide ranked by ideXlab platform
Zibin Zheng - One of the best experts on this subject based on the ideXlab platform.
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ICWS - Asymmetric Correlation Regularized Matrix Factorization for Web Service Recommendation
2016 IEEE International Conference on Web Services (ICWS), 2016Co-Authors: Shenglin Zhao, Zibin ZhengAbstract:Web Service Recommendation has recently drawn much attention with the growing amount of Web Services. Previous work usually exploits the collaborative filtering techniques for Web Service Recommendation, but suffers from the data sparsity problem that leads to inaccurate results. Our analysis on a real-world Quality of Service (QoS) dataset shows that there is a hidden correlation among users and Services. We define such hidden correlation with an asymmetric matrix (namely asymmetric correlation), in which each entry presents the hidden correlation between a user pair or between a Service pair. The goal of this work is to employ such asymmetric correlation among users and Services to alleviate the data sparsity problem and further enhance the prediction accuracy in Service Recommendation. Specifically, we propose an asymmetric correlation regularized matrix factorization (MF) framework, in which asymmetric correlation and asymmetric correlation propagation have been naturally integrated. Finally, experimental results on a well-known real-world QoS dataset validate that the use of asymmetric correlation among users and Services is effective in improving prediction accuracy for Web Service Recommendation.
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Asymmetric Correlation Regularized Matrix Factorization for Web Service Recommendation
2016 IEEE International Conference on Web Services (ICWS), 2016Co-Authors: Shenglin Zhao, Zibin ZhengAbstract:Web Service Recommendation has recently drawn much attention with the growing amount of Web Services. Previous work usually exploits the collaborative filtering techniques for Web Service Recommendation, but suffers from the data sparsity problem that leads to inaccurate results. Our analysis on a real-world Quality of Service (QoS) dataset shows that there is a hidden correlation among users and Services. We define such hidden correlation with an asymmetric matrix (namely asymmetric correlation), in which each entry presents the hidden correlation between a user pair or between a Service pair. The goal of this work is to employ such asymmetric correlation among users and Services to alleviate the data sparsity problem and further enhance the prediction accuracy in Service Recommendation. Specifically, we propose an asymmetric correlation regularized matrix factorization (MF) framework, in which asymmetric correlation and asymmetric correlation propagation have been naturally integrated. Finally, experimental results on a well-known real-world QoS dataset validate that the use of asymmetric correlation among users and Services is effective in improving prediction accuracy for Web Service Recommendation.
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Location-aware and personalized collaborative filtering for web Service Recommendation
IEEE Transactions on Services Computing, 2016Co-Authors: Mingdong Tang, Zibin ZhengAbstract:Collaborative Filtering (CF) is widely employed for making Web Service Recommendation. CF-based Web Service Recommendation aims to predict missing QoS (Quality-of-Service) values of Web Services. Although several CF-based Web Service QoS prediction methods have been proposed in recent years, the performance still needs significant improvement. First, existing QoS prediction methods seldom consider personalized influence of users and Services when measuring the similarity between users and between Services. Second, Web Service QoS factors, such as response time and throughput, usually depends on the locations of Web Services and users. However, existing Web Service QoS prediction methods seldom took this observation into consideration. In this paper, we propose a location-aware personalized CF method for Web Service Recommendation. The proposed method leverages both locations of users and Web Services when selecting similar neighbors for the target user or Service. The method also includes an enhanced similarity measurement for users and Web Services, by taking into account the personalized influence of them. To evaluate the performance of our proposed method, we conduct a set of comprehensive experiments using a real-world Web Service dataset. The experimental results indicate that our approach improves the QoS prediction accuracy and computational efficiency significantly, compared to previous CF-based methods.
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A Privacy-Preserving QoS Prediction Framework for Web Service Recommendation
2015 IEEE International Conference on Web Services, 2015Co-Authors: Pinjia He, Zibin ZhengAbstract:QoS-based Web Service Recommendation has recently gained much attention for providing a promising way to help users find high-quality Services. To facilitate such Recommendations, existing studies suggest the use of collaborative filtering techniques for personalized QoS prediction. These approaches, by leveraging partially observed QoS values from users, can achieve high accuracy of QoS predictions on the unobserved ones. However, the requirement to collect users' QoS data likely puts user privacy at risk, thus making them unwilling to contribute their usage data to a Web Service recommender system. As a result, privacy becomes a critical challenge in developing practical Web Service recommender systems. In this paper, we make the first attempt to cope with the privacy concerns for Web Service Recommendation. Specifically, we propose a simple yet effective privacy-preserving framework by applying data obfuscation techniques, and further develop two representative privacy-preserving QoS prediction approaches under this framework. Evaluation results from a publicly-available QoS dataset of real-world Web Services demonstrate the feasibility and effectiveness of our privacy-preserving QoS prediction approaches. We believe our work can serve as a good starting point to inspire more research efforts on privacy-preserving Web Service Recommendation.
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Reputation Measurement and Malicious Feedback Rating Prevention in Web Service Recommendation Systems
IEEE Transactions on Services Computing, 2015Co-Authors: Shangguang Wang, Zibin Zheng, Zhengping Wu, Fangchun YangAbstract:Web Service Recommendation systems can help Service users to locate the right Service from the large number of available web Services. Avoiding recommending dishonest or unsatisfactory Services is a fundamental research problem in the design of web Service Recommendation systems. Reputation of web Services is a widely-employed metric that determines whether the Service should be recommended to a user. The Service reputation score is usually calculated using feedback ratings provided by users. Although the reputation measurement of web Service has been studied in the recent literature, existing malicious and subjective user feedback ratings often lead to a bias that degrades the performance of the Service Recommendation system. In this paper, we propose a novel reputation measurement approach for web Service Recommendations. We first detect malicious feedback ratings by adopting the cumulative sum control chart, and then we reduce the effect of subjective user feedback preferences employing the Pearson Correlation Coefficient. Moreover, in order to defend malicious feedback ratings, we propose a malicious feedback rating prevention scheme employing Bloom filtering to enhance the Recommendation performance. Extensive experiments are conducted by employing a real feedback rating data set with 1.5 million web Service invocation records. The experimental results show that our proposed measurement approach can reduce the deviation of the reputation measurement and enhance the success ratio of the web Service Recommendation.
Mingdong Tang - One of the best experts on this subject based on the ideXlab platform.
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Diversifying web Service Recommendation results via exploring Service usage history
IEEE Transactions on Services Computing, 2016Co-Authors: Guosheng Kang, Mingdong TangAbstract:The last decade has witnessed a tremendous growth of web Services as a major technology for sharing data, computing resources, and programs on the web. With the increasing adoption and presence of web Services, design of novel approaches for effective web Service Recommendation to satisfy users' potential requirements has become of paramount importance. Existing web Service Recommendation approaches mainly focus on predicting missing QoS values of web Service candidates which are interesting to a user using collaborative filtering approach, content-based approach, or their hybrid. These Recommendation approaches assume that recommended web Services are independent to each other, which sometimes may not be true. As a result, many similar or redundant web Services may exist in a Recommendation list. In this paper, we propose a novel web Service Recommendation approach incorporating a user's potential QoS preferences and diversity feature of user interests on web Services. User's interests and QoS preferences on web Services are first mined by exploring the web Service usage history. Then we compute scores of web Service candidates by measuring their relevance with historical and potential user interests, and their QoS utility. We also construct a web Service graph based on the functional similarity between web Services. Finally, we present an innovative diversity-aware web Service ranking algorithm to rank the web Service candidates based on their scores, and diversity degrees derived from the web Service graph. Extensive experiments are conducted based on a real world web Service dataset, indicating that our proposed web Service Recommendation approach significantly improves the quality of the Recommendation results compared with existing methods.
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Location-aware and personalized collaborative filtering for web Service Recommendation
IEEE Transactions on Services Computing, 2016Co-Authors: Mingdong Tang, Zibin ZhengAbstract:Collaborative Filtering (CF) is widely employed for making Web Service Recommendation. CF-based Web Service Recommendation aims to predict missing QoS (Quality-of-Service) values of Web Services. Although several CF-based Web Service QoS prediction methods have been proposed in recent years, the performance still needs significant improvement. First, existing QoS prediction methods seldom consider personalized influence of users and Services when measuring the similarity between users and between Services. Second, Web Service QoS factors, such as response time and throughput, usually depends on the locations of Web Services and users. However, existing Web Service QoS prediction methods seldom took this observation into consideration. In this paper, we propose a location-aware personalized CF method for Web Service Recommendation. The proposed method leverages both locations of users and Web Services when selecting similar neighbors for the target user or Service. The method also includes an enhanced similarity measurement for users and Web Services, by taking into account the personalized influence of them. To evaluate the performance of our proposed method, we conduct a set of comprehensive experiments using a real-world Web Service dataset. The experimental results indicate that our approach improves the QoS prediction accuracy and computational efficiency significantly, compared to previous CF-based methods.
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ICWS - Combining Global and Local Trust for Service Recommendation
2014 IEEE International Conference on Web Services, 2014Co-Authors: Mingdong Tang, Yu Xu, Zibin ZhengAbstract:Recommending trusted Services to users is of paramount value in Service-oriented environments. Reputation has been widely used to measure the trustworthiness of Services, and various reputation models for Service Recommendation have been proposed. Reputation is basically a global trust score obtained by aggregating trust from a community of users, which could be conflicting with an individual's personal opinion on the Service. Evaluating a Service's trustworthiness locally based on the evaluating user's own or his/her friends' experiences is sometimes more accurate. However, local trust assessment may fail to work when no trust path from an evaluating user to a target Service exists. This paper proposes a hybrid trust-aware Service Recommendation method for Service-oriented environment with social networks via combining global trust and local trust evaluation. A global trust metric and a local trust metric are firstly presented, and then a strategy for combining them to predict the final trust of Service is proposed. To evaluate the proposed method's performance, we conducted several simulations based on a synthesized dataset. The simulation results show that our proposed method outperforms the other methods in Service Recommendation.
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Combining Global and Local Trust for Service Recommendation
2014 IEEE International Conference on Web Services, 2014Co-Authors: Mingdong Tang, Yu Xu, Zibin ZhengAbstract:Recommending trusted Services to users is of paramount value in Service-oriented environments. Reputation has been widely used to measure the trustworthiness of Services, and various reputation models for Service Recommendation have been proposed. Reputation is basically a global trust score obtained by aggregating trust from a community of users, which could be conflicting with an individual's personal opinion on the Service. Evaluating a Service's trustworthiness locally based on the evaluating user's own or his/her friends' experiences is sometimes more accurate. However, local trust assessment may fail to work when no trust path from an evaluating user to a target Service exists. This paper proposes a hybrid trust-aware Service Recommendation method for Service-oriented environment with social networks via combining global trust and local trust evaluation. A global trust metric and a local trust metric are firstly presented, and then a strategy for combining them to predict the final trust of Service is proposed. To evaluate the proposed method's performance, we conducted several simulations based on a synthesized dataset. The simulation results show that our proposed method outperforms the other methods in Service Recommendation.
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IEEE SCC - Trust-Aware Service Recommendation via Exploiting Social Networks
2013 IEEE International Conference on Services Computing, 2013Co-Authors: Mingdong Tang, Yu Xu, Zibin ZhengAbstract:With the rapid growth in the number of available Services, recommending suitable Services to users becomes increasingly important. A number of collaborative Service Recommendation methods based on user experiences have been proposed for this purpose. Most of them adopt the similarity-based Collaborative Filtering (CF) technique, which tends to identify similar users for a target user and recommends to the target user the Services preferred by the similar users. However, a user similar to the target user is unnecessarily trustworthy to him/her. Therefore, the results recommended by similarity-based CF are probably unreliable. Moreover, existing Service Recommendation methods seldom incorporate social trust relationships among Service users into Service Recommendation. In this paper, we propose a collaborative, trust-aware Service Recommendation method for Service-oriented environments with social networks. The method is based on an integration of the user-Service relation and the user-user social relation. Experimental results demonstrate that our Service Recommendation method significantly outperforms conventional similarity-based Recommendation and trust-based Service Recommendation methods.
Jia Zhang - One of the best experts on this subject based on the ideXlab platform.
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web Service Recommendation with reconstructed profile from mashup descriptions
IEEE Transactions on Automation Science and Engineering, 2018Co-Authors: Yang Zhong, Jia ZhangAbstract:Web Services are self-contained software components that support business process automation over the Internet, and mashup is a popular technique that creates value-added Service compositions to fulfill complicated business requirements. For mashup developers, looking for desired component Services from a sea of Service candidates is often challenging. Therefore, web Service Recommendation has become a highly demanding technique. Traditional approaches, however, mostly rely on static and potentially subjectively described texts offered by Service providers. In this paper, we propose a novel way of dynamically reconstructing objective Service profiles based on mashup descriptions, which carry historical information of how Services are used in mashups. Our key idea is to leverage mashup descriptions and structures to discover important word features of Services and bridge the vocabulary gap between mashup developers and Service providers. Specifically, we jointly model mashup descriptions and component Service using author topic model in order to reconstruct Service profiles. Exploiting word features derived from the reconstructed Service profiles, a new Service Recommendation algorithm is developed. Experiments over a real-world data set from ProgrammableWeb.com demonstrate that our proposed Service Recommendation algorithm is effective and outperforms the state-of-the-art methods. Note to Practitioners —Service Recommendation accuracy for mashup creation is often limited due to poor quality of Service descriptions. Mashup descriptions contain valuable information about functions and features of its component Services, which can be leveraged to enhance descriptive quality of original Service profiles. Based on the assumption, this paper proposes a novel two-phase Service Recommendation framework to facilitate mashup creation. Specifically, our approach reconstructs Service profiles by extracting appropriate words from historical mashup descriptions. Then, a novel Service Recommendation algorithm is developed by exploiting popularity and relevance measures hidden in the reconstructed profiles. Moreover, we propose the rules of dominant words discovery and employ it to further refine our algorithm.
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Web Service Recommendation With Reconstructed Profile From Mashup Descriptions
IEEE Transactions on Automation Science and Engineering, 2018Co-Authors: Yang Zhong, Jia ZhangAbstract:Web Services are self-contained software components that support business process automation over the Internet, and mashup is a popular technique that creates value-added Service compositions to fulfill complicated business requirements. For mashup developers, looking for desired component Services from a sea of Service candidates is often challenging. Therefore, web Service Recommendation has become a highly demanding technique. Traditional approaches, however, mostly rely on static and potentially subjectively described texts offered by Service providers. In this paper, we propose a novel way of dynamically reconstructing objective Service profiles based on mashup descriptions, which carry historical information of how Services are used in mashups. Our key idea is to leverage mashup descriptions and structures to discover important word features of Services and bridge the vocabulary gap between mashup developers and Service providers. Specifically, we jointly model mashup descriptions and component Service using author topic model in order to reconstruct Service profiles. Exploiting word features derived from the reconstructed Service profiles, a new Service Recommendation algorithm is developed. Experiments over a real-world data set from ProgrammableWeb.com demonstrate that our proposed Service Recommendation algorithm is effective and outperforms the state-of-the-art methods.
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Time-aware Service Recommendation for mashup creation
IEEE Transactions on Services Computing, 2015Co-Authors: Yang Zhong, Keman Huang, Wei Tan, Yushun Fan, Jia ZhangAbstract:© 2014 IEEE. Web Service Recommendation has become increasingly important as Services become increasingly prevalent on the Internet. Existing methods either focus on content matching techniques such as keyword search and semantic matching, or rely on Quality of Service (QoS) prediction. However, the fact that Services and their mashups typically evolve over time has not been given sufficient attention. We argue that a practical Service Recommendation approach should take into account the evolution of Services in the context of a Service ecosystem. In this paper, we present a method to extract Service evolution patterns by exploiting Latent Dirichlet Allocation (LDA) and time series prediction. Based on it, we have developed a time-aware Service Recommendation framework guiding mashup creation seamlessly integrating Service evolution, collaborative filtering and content matching. Experiments on real-world ProgrammableWeb data set show that our approach leads to a higher precision than traditional collaborative filtering and content matching methods.
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Time-Aware Service Recommendation for Mashup Creation
IEEE Transactions on Services Computing, 2015Co-Authors: Yang Zhong, Keman Huang, Jia ZhangAbstract:Web Service Recommendation has become a critical problem as Services become increasingly prevalent on the Internet. Some existing methods focus on content matching techniques, while others are based on QoS measurement. However, Service ecosystem is evolving over time with Services publishing, prospering and perishing. Few existing methods consider or exploit the evolution of Service ecosystem on Service Recommendation. This paper employs a probabilistic approach to predict the popularity of Services to enhance the Recommendation performance. A method is presented that extracts Service evolution patterns by exploiting latent dirichlet allocation (LDA) and time series prediction. A time-aware Service Recommendation framework is established for mashup creation that conducts joint analysis of temporal information, content description and historical mashup-Service usage in an evolving Service ecosystem. Experiments on a real-world Service repository, ProgrammableWeb.com, show that the proposed approach leads to a higher precision than traditional collaborative filtering and content matching methods, by taking into account temporal information.
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ICWS - Domain-Aware Service Recommendation for Service Composition
2014 IEEE International Conference on Web Services, 2014Co-Authors: Cheng Wu, Keman Huang, Jia ZhangAbstract:Service compositions inherently require multiple Services each with its domain-specific functionality. Therefore, how to mine matching patterns between Services in relevant domains and compositions becomes crucial to Service Recommendation for composition. Existing methods usually overlook domain relevance and domain-specific matching patterns, which restrict the quality of Recommendations. In this paper, a novel approach is proposed to offer domain-aware Service Recommendation. First, a K Nearest Neighbor variant (vKNN) based on topic model Latent Dirichlet Allocation (LDA) is introduced to cluster Services into semantically coherent domains. On top of Service domain clustering results by vKNN, a probabilistic matching model Domain Router (DR) based on Extreme Learning Machine (ELM) is developed for decomposing a requirement to relevant domains. Finally, a comprehensive Domain Topic Matching (DTM) model is built to mine relevant domain-specific matching patterns to facilitate Service Recommendation. Experiments on a large-scale real-world dataset show that DTM not only gains significant improvement at precision rate but also enhances the diversity of results.
Lianyong Qi - One of the best experts on this subject based on the ideXlab platform.
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TrustCom/BigDataSE - Accuracy-Aware Service Recommendation with Privacy
2019 18th IEEE International Conference On Trust Security And Privacy In Computing And Communications 13th IEEE International Conference On Big Data S, 2019Co-Authors: Lianyong QiAbstract:With the advent of IoT(Internet of Things) age, the variety and volume of web Services have been increasing at an fast speed. This often leads to a heavy burden on users' Service selections. Under this circumstances, a variety of methods such as Collaborative Filtering are adopted to deal with this challenging situation. While traditional Collaborative Filtering method has some shortcomings, one of which is that only centralized user-Service data are considered while distributed quality data from multiple platform are ignored. Generally, Service Recommendation across different platforms often involves the data communication among the multiple platforms, during which user privacy may be disclosed and much computational time is required. Considering these challenges, a novel amplified LSH(Locality-Snsitive)-based Service Recommendation method, i.e., SR Amplified-LSH, is proposed in this paper. SR Amplified-LSH can achieve a good balance among Recommendation accuracy, efficiency and user privacy. Finally, extensive experiments deployed on MovieLens dataset validate the feasibility of our proposed method.
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time aware distributed Service Recommendation with privacy preservation
Information Sciences, 2019Co-Authors: Lianyong Qi, Qiang He, Ruili Wang, Chunhua Hu, Shancang Li, Xiaolong XuAbstract:Abstract As a promising way to extract insightful information from massive data, Service Recommendation has gained ever-increasing attentions in both academic and industrial areas. Recently, the Locality-Sensitive Hashing (LSH) technique is introduced into Service Recommendation to pursue high Recommendation efficiency and the capability of privacy-preservation, especially when the historical Service quality (QoS) data used to make Recommendation decisions are distributed across different platforms. However, existing LSH-based Service Recommendation approaches often face the following challenge: they often assume that the QoS data for Service Recommendation are static and unique, without considering the influence of dynamic context (e.g., time) on QoS. In view of this challenge, we extend the traditional LSH technique to incorporate the time factor and further propose a novel time-aware and privacy-preserving Service Recommendation approach based on LSH. Finally, we conduct extensive experiments on a large-scale real-world dataset, i.e., WS-DREAM, to validate the effectiveness and efficiency of our proposal. The experiment results show that our approach achieves a good tradeoff between Recommendation accuracy and efficiency while guaranteeing privacy-preservation.
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Personalised Service Recommendation process based on Service clustering
International Journal of Computational Science and Engineering, 2019Co-Authors: Jiguo Yu, Lianyong QiAbstract:Personalised Service Recommendation is the key technology for Service platforms; the demand preferences of users are the important factors for personalised Recommendation. First, in order to improve accuracy and adaptability of Service Recommendation, Services are needed to be initialised before being recommended and selected, then they are classified and clustered according to demand preferences, and Service clusters are defined and demonstrated. In the sparse problem of Service function matrix, historical and potential preferences are expressed as double matrices. Second, Service cluster is viewed as the basic business unit, we optimise graph summarisation algorithm and construct Service Recommendation algorithm SCRP, helped by the experiments about variety parameters, which has more advantages than other algorithms. Third, we select fuzzy degree and difference to be the two key indicators, and use some Service clusters to complete simulating and analyse algorithm performances. The results show that our Service selection and Recommendation method is better than others, which might effectively improve the quality of Service Recommendation.
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Accuracy-Aware Service Recommendation with Privacy
2019 18th IEEE International Conference On Trust Security And Privacy In Computing And Communications 13th IEEE International Conference On Big Data S, 2019Co-Authors: Lianyong QiAbstract:With the advent of IoT(Internet of Things) age, the variety and volume of web Services have been increasing at an fast speed. This often leads to a heavy burden on users' Service selections. Under this circumstances, a variety of methods such as Collaborative Filtering are adopted to deal with this challenging situation. While traditional Collaborative Filtering method has some shortcomings, one of which is that only centralized user-Service data are considered while distributed quality data from multiple platform are ignored. Generally, Service Recommendation across different platforms often involves the data communication among the multiple platforms, during which user privacy may be disclosed and much computational time is required. Considering these challenges, a novel amplified LSH(Locality-Snsitive)-based Service Recommendation method, i.e., SR Amplified-LSH, is proposed in this paper. SRAmplified-LSH can achieve a good balance among Recommendation accuracy, efficiency and user privacy. Finally, extensive experiments deployed on MovieLens dataset validate the feasibility of our proposed method.
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SpaCCS - Amplified locality-sensitive hashing for privacy-preserving distributed Service Recommendation
Security Privacy and Anonymity in Computation Communication and Storage, 2017Co-Authors: Lianyong Qi, Xuyun Zhang, Shui YuAbstract:With the ever-increasing volume of Services registered in various web communities, Service Recommendation techniques, e.g., Collaborative Filtering (i.e., CF) have provided a promising way to alleviate the heavy burden on the Service selection decisions of target users. However, traditional CF-based Service Recommendation approaches often assume that the Recommendation bases, i.e., historical Service quality data are centralized, without considering the distributed Service Recommendation scenarios as well as the resulted privacy leakage risks. In view of this shortcoming, Locality-Sensitive Hashing (LSH) technique is recruited in this paper to protect the private information of users when distributed Service Recommendations are made. Furthermore, LSH is essentially a probability-based search technique and hence may generate “False-positive” or “False-negative” recommended results; therefore, we amplify LSH by AND/OR operations to improve the Recommendation accuracy. Finally, through a set of experiments deployed on a real distributed Service quality dataset, i.e., WS-DREAM, we validate the feasibility of our proposed Recommendation approach named DistSR Amplify-LSH in terms of Recommendation accuracy and efficiency while guaranteeing privacy-preservation in the distributed environment.
Yang Zhong - One of the best experts on this subject based on the ideXlab platform.
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web Service Recommendation with reconstructed profile from mashup descriptions
IEEE Transactions on Automation Science and Engineering, 2018Co-Authors: Yang Zhong, Jia ZhangAbstract:Web Services are self-contained software components that support business process automation over the Internet, and mashup is a popular technique that creates value-added Service compositions to fulfill complicated business requirements. For mashup developers, looking for desired component Services from a sea of Service candidates is often challenging. Therefore, web Service Recommendation has become a highly demanding technique. Traditional approaches, however, mostly rely on static and potentially subjectively described texts offered by Service providers. In this paper, we propose a novel way of dynamically reconstructing objective Service profiles based on mashup descriptions, which carry historical information of how Services are used in mashups. Our key idea is to leverage mashup descriptions and structures to discover important word features of Services and bridge the vocabulary gap between mashup developers and Service providers. Specifically, we jointly model mashup descriptions and component Service using author topic model in order to reconstruct Service profiles. Exploiting word features derived from the reconstructed Service profiles, a new Service Recommendation algorithm is developed. Experiments over a real-world data set from ProgrammableWeb.com demonstrate that our proposed Service Recommendation algorithm is effective and outperforms the state-of-the-art methods. Note to Practitioners —Service Recommendation accuracy for mashup creation is often limited due to poor quality of Service descriptions. Mashup descriptions contain valuable information about functions and features of its component Services, which can be leveraged to enhance descriptive quality of original Service profiles. Based on the assumption, this paper proposes a novel two-phase Service Recommendation framework to facilitate mashup creation. Specifically, our approach reconstructs Service profiles by extracting appropriate words from historical mashup descriptions. Then, a novel Service Recommendation algorithm is developed by exploiting popularity and relevance measures hidden in the reconstructed profiles. Moreover, we propose the rules of dominant words discovery and employ it to further refine our algorithm.
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Web Service Recommendation With Reconstructed Profile From Mashup Descriptions
IEEE Transactions on Automation Science and Engineering, 2018Co-Authors: Yang Zhong, Jia ZhangAbstract:Web Services are self-contained software components that support business process automation over the Internet, and mashup is a popular technique that creates value-added Service compositions to fulfill complicated business requirements. For mashup developers, looking for desired component Services from a sea of Service candidates is often challenging. Therefore, web Service Recommendation has become a highly demanding technique. Traditional approaches, however, mostly rely on static and potentially subjectively described texts offered by Service providers. In this paper, we propose a novel way of dynamically reconstructing objective Service profiles based on mashup descriptions, which carry historical information of how Services are used in mashups. Our key idea is to leverage mashup descriptions and structures to discover important word features of Services and bridge the vocabulary gap between mashup developers and Service providers. Specifically, we jointly model mashup descriptions and component Service using author topic model in order to reconstruct Service profiles. Exploiting word features derived from the reconstructed Service profiles, a new Service Recommendation algorithm is developed. Experiments over a real-world data set from ProgrammableWeb.com demonstrate that our proposed Service Recommendation algorithm is effective and outperforms the state-of-the-art methods.
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Time-aware Service Recommendation for mashup creation
IEEE Transactions on Services Computing, 2015Co-Authors: Yang Zhong, Keman Huang, Wei Tan, Yushun Fan, Jia ZhangAbstract:© 2014 IEEE. Web Service Recommendation has become increasingly important as Services become increasingly prevalent on the Internet. Existing methods either focus on content matching techniques such as keyword search and semantic matching, or rely on Quality of Service (QoS) prediction. However, the fact that Services and their mashups typically evolve over time has not been given sufficient attention. We argue that a practical Service Recommendation approach should take into account the evolution of Services in the context of a Service ecosystem. In this paper, we present a method to extract Service evolution patterns by exploiting Latent Dirichlet Allocation (LDA) and time series prediction. Based on it, we have developed a time-aware Service Recommendation framework guiding mashup creation seamlessly integrating Service evolution, collaborative filtering and content matching. Experiments on real-world ProgrammableWeb data set show that our approach leads to a higher precision than traditional collaborative filtering and content matching methods.
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Time-Aware Service Recommendation for Mashup Creation
IEEE Transactions on Services Computing, 2015Co-Authors: Yang Zhong, Keman Huang, Jia ZhangAbstract:Web Service Recommendation has become a critical problem as Services become increasingly prevalent on the Internet. Some existing methods focus on content matching techniques, while others are based on QoS measurement. However, Service ecosystem is evolving over time with Services publishing, prospering and perishing. Few existing methods consider or exploit the evolution of Service ecosystem on Service Recommendation. This paper employs a probabilistic approach to predict the popularity of Services to enhance the Recommendation performance. A method is presented that extracts Service evolution patterns by exploiting latent dirichlet allocation (LDA) and time series prediction. A time-aware Service Recommendation framework is established for mashup creation that conducts joint analysis of temporal information, content description and historical mashup-Service usage in an evolving Service ecosystem. Experiments on a real-world Service repository, ProgrammableWeb.com, show that the proposed approach leads to a higher precision than traditional collaborative filtering and content matching methods, by taking into account temporal information.
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Time-Aware Service Recommendation for Mashup Creation in an Evolving Service Ecosystem
2014 IEEE International Conference on Web Services, 2014Co-Authors: Yang Zhong, Keman Huang, Jia ZhangAbstract:Web Service Recommendation has become a critical problem as Services become increasingly prevalent on the Internet. Some existing methods focus on content matching techniques such as keyword search and semantic matching while others are based on Quality of Service (QoS) prediction. However, Services and their mashups are evolving over time with publishing, perishing and changing of interfaces. Therefore, a practical Service Recommendation approach should take into account the evolution of a Service ecosystem. In this paper, we present a method to extract Service evolution patterns by exploiting Latent Dirichlet Allocation (LDA) and time series prediction. A time-aware Service Recommendation framework for mashup creation is presented combing Service evolution, collaborative filtering and content matching. Experiments on real-world ProgrammableWeb data set show that our approach leads to a higher precision than traditional collaborative filtering and content matching methods.
