List of Titles

Diversified and scalable service recommendation with accuracy guarantee

- Wang, Lina, Zhang, Xuyun, Wang, Tian, Wan, Shaohua, Pang, Shaoning

Authors: Wang, Lina , Zhang, Xuyun , Wang, Tian , Wan, Shaohua , Pang, Shaoning
Date: 2021
Type: Text , Journal article
Relation: IEEE Transactions on Computational Social Systems Vol. 8, no. 5 (2021), p. 1182-1193
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Reviewed:
Description: As one of the most successful recommendation techniques, neighborhood-based collaborative filtering (CF), which recommends appropriate items to a target user by identifying similar users or similar items, has been widely applied to various recommender systems. Although many neighbor-based CF methods have been put forward, there are still some open issues that have remained unsolved. First, the ever-increasing volume of user-item rating data decreases the recommendation efficiency significantly as a recommender system needs to analyze all the rating data when searching for similar neighbors or similar items. In this situation, users' requirements on quick response may not be met. Second, in neighbor-based CF methods, more attention is paid to the recommendation accuracy while other key indicators of recommendation performances are often ignored, i.e., recommendation diversity (RD), which probably produces similar or redundant items in the recommended list and decreases users' satisfaction. Considering these issues, a diversified and scalable recommendation method (called DR_LT) based on locality-sensitive hashing and cover tree is proposed in this article, where the item topic information is used to optimize the final recommended list. We show the effectiveness of our proposed method through a set of experiments on MovieLens data set that clearly shows the feasibility of our proposal in terms of item recommendation accuracy, diversity, and scalability. © 2014 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Shaoning Pang” is provided in this record**

Keyword search for building service-based systems

- He, Qiang, Zhou, Rui, Zhang, Xuyun, Wang, Yanchun, Ye, Dayong, Chen, Feifei, Grundy, John, Yang, Yun

Authors: He, Qiang , Zhou, Rui , Zhang, Xuyun , Wang, Yanchun , Ye, Dayong , Chen, Feifei , Grundy, John , Yang, Yun
Date: 2017
Type: Text , Journal article
Relation: IEEE Transactions on Software Engineering Vol. 43, no. 7 (2017), p. 658-674
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Reviewed:
Description: With the fast growth of applications of service-oriented architecture (SOA) in software engineering, there has been a rapid increase in demand for building service-based systems (SBSs) by composing existing Web services. Finding appropriate component services to compose is a key step in the SBS engineering process. Existing approaches require that system engineers have detailed knowledge of SOA techniques which is often too demanding. To address this issue, we propose Keyword Search for Service-based Systems (KS3), a novel approach that integrates and automates the system planning, service discovery and service selection operations for building SBSs based on keyword search. KS3 assists system engineers without detailed knowledge of SOA techniques in searching for component services to build SBSs by typing a few keywords that represent the tasks of the SBSs with quality constraints and optimisation goals for system quality, e.g., reliability, throughput and cost. KS3 offers a new paradigm for SBS engineering that can significantly save the time and effort during the system engineering process. We conducted large-scale experiments using two real-world Web service datasets to demonstrate the practicality, effectiveness and efficiency of KS3. © 1976-2012 IEEE.

Personalized quality centric service recommendation

- Zhang, Yiwen, Ai, Xiaofei, He, Qiang, Zhang, Xuyun, Dou, Wanchun, Chen, Feifei, Chen, Liang, Yang, Yun

Authors: Zhang, Yiwen , Ai, Xiaofei , He, Qiang , Zhang, Xuyun , Dou, Wanchun , Chen, Feifei , Chen, Liang , Yang, Yun
Date: 2017
Type: Text , Conference proceedings
Relation: 15th International Conference on Service-Oriented Computing, ICSOC 2017; Malaga, Spain; 13th-16th November 2017; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10601 LNCS, p. 528-544
Full Text: false
Reviewed:
Description: The broad application of service-oriented architecture (SOA) has fueled the rapid growth of web and cloud services and service-based systems (SBSs). Tremendous web and cloud services have been deployed all over the world. Finding the right services becomes difficult and critical. Thus, service recommendation has become of paramount research and practical importance. Existing web service recommendation approaches employ utility functions or skyline techniques. However, those approaches have not addressed a critical and fundamental problem: how to recommend services according to a system engineer’s quality constraints, e.g., response time, failure rate, etc. To address this issue, we first propose two basic personalized quality centric approaches for service recommendation, which employ the k-nearest neighbours and the dynamic skyline techniques respectively. To overcome the respective limitations of the two basic approaches, we propose two hybrid approaches, namely KNN-DSL and DSL-KNN. Extensive experiments are conducted on a real-world dataset to demonstrate the effectiveness and efficiency of our approaches. © Springer International Publishing AG 2017.
Description: The broad application of service-oriented architecture (SOA) has fueled the rapid growth of web and cloud services and service-based systems (SBSs). Tremendous web and cloud services have been deployed all over the world. Finding the right services becomes difficult and critical. Thus, service recommendation has become of paramount research and practical importance. Existing web service recommendation approaches employ utility functions or skyline techniques. However, those approaches have not addressed a critical and fundamental problem: how to recommend services according to a system engineer’s quality constraints, e.g., response time, failure rate, etc. To address this issue, we first propose two basic personalized quality centric approaches for service recommendation, which employ the k-nearest neighbors and the dynamic skyline techniques respectively. To overcome the respective limitations of the two basic approaches, we propose two hybrid approaches, namely KNN-DSL and DSL-KNN. Extensive experiments are conducted on a real-world dataset to demonstrate the effectiveness and efficiency of our approaches. © Springer International Publishing AG 2017.

Privacy-aware data fusion and prediction with spatial-temporal context for smart city industrial environment

- Qi, Lianyong, Hu, Chunhua, Zhang, Xuyun, Khosravi, Mohammad, Pang, Shaoning

Authors: Qi, Lianyong , Hu, Chunhua , Zhang, Xuyun , Khosravi, Mohammad , Pang, Shaoning
Date: 2021
Type: Text , Journal article
Relation: IEEE Transactions on Industrial Informatics Vol. 17, no. 6 (2021), p. 4159-4167
Full Text: false
Reviewed:
Description: As one of the cyber-physical-social systems that plays a key role in people's daily activities, a smart city is producing a considerable amount of industrial data associated with transportation, healthcare, business, social activities, and so on. Effectively and efficiently fusing and mining such data from multiple sources can contribute much to the development and improvements of various smart city applications. However, the industrial data collected from the smart city are often sensitive and contain partial user privacy such as spatial-temporal context information. Therefore, it is becoming a necessity to secure user privacy hidden in the smart city data before these data are integrated together for further mining, analyses, and prediction. However, due to the inherent tradeoff between data privacy and data availability, it is often a challenging task to protect users' context privacy while guaranteeing accurate data analysis and prediction results after data fusion. Considering this challenge, a novel privacy-aware data fusion and prediction approach for the smart city industrial environment is put forward in this article, which is based on the classic locality-sensitive hashing technique. At last, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results show better prediction performances of our approach compared to other competitive ones. © 2005-2012 IEEE. *Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Shaoning Pang” is provided in this record**

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