Social Networks: Service Selection and Recommendation

Transcription

1 Social Networks: Service Selection and Recommendation Jebrin Al-Sharawneh Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Information Systems) Faculty of Engineering and Information Technology University of Technology, Sydney September, 2012

2 Abstract The Service-Oriented Computing paradigm is widely acknowledged for its potential to revolutionize the world of computing through the utilization of Web services. It is expected that Web services will fully leverage the Semantic Web to outsource some of their functionalities to other Web services that provide value-added services, and by integrating the business logic of Web services in the form of business to business and business to consumer e-commerce applications. In the Service Web, Web services and Web-Based Social Networks are emerging in which a wide range of similar functionalities are expected to be offered by a vast number of Web services, and applications can search and compose services according to users needs in a seamless and an automatic fashion. Web services are expected to outsource some of their functionalities to other Web services. In such situations, some services may be new to the service market, and some may act maliciously in order to be selected. A key requirement is to provide mechanisms for quality selection and recommendation of relevant Web services with perceived risk considerations. Although the future of Web service selection and recommendation looks promising, there are challenging issues related to user knowledge and behavior, as well as issues related to recommendation approaches. This dissertation addresses the demanding issues in Web service selection and recommendation from theory and practice perspectives. These challenges include cold-start users, who represent more than 50% of the social network population, the capture of users preferences, risk mitigation in service selection, customers privacy and application scalability. This dissertation proposes a novel approach to automate social-based Web service selection and recommendation in a dynamic environment. It utilizes Web-Based Social Networks and the Follow the Leader strategy, for a Credibility-based framework that includes two credibility models: the user Credibility model which is used to qualify i

3 consumers as either leaders or followers based on their credibility, and the service Credibility model which is used to identify the best services that act as market leaders. Experimental evaluation results demonstrate that the social network service selection and recommendation approach utilizing the credibility-based framework and Follow the Leader strategy provides an efficient, effective and scalable provision of credible services, especially for cold-start users. The research results take a further step towards developing a social-based automated and dynamically adaptive Web service selection and recommendation system in the future. ii

4 Certificate of Authorship/Originality I certify that the work in this thesis has not previously been submitted for a degree nor has it been submitted as part of requirements for a degree except as fully acknowledged within the text. I also certify that the thesis has been written by me. Any help that I have received in my research work and the preparation of the thesis itself has been acknowledged. In addition, I certify that all information sources and literature used are indicated in the thesis. Jebrin Al-Sharawneh September, 2012 iii

5 To the souls of my parents who taught me to love learning iv

6 Acknowledgments Although I planned to complete this degree thirty five years ago, as the Italian proverb says The right time comes but once. I think this research project would not have been possible without the support of the following people: First and foremost, I would like to express my gratitude to my supervisor Professor Mary- Anne Williams for her wise advice, careful guidance, useful connections, constant support and enduring patience throughout my studies and while writing this dissertation. Mary- Anne s advice on the use of Follow the Leader strategy was the turning point of my research. Learning how to research and produce publications is much easier with a supervisor who has as much experience and enthusiasm as Mary-Anne. I would like also to thank my co-supervisor, Associate Professor David Goldbaum for giving me the opportunity to work with him on the simulations of Follow the Leader project and for his invaluable inputs on Follow the Leader strategy. I must also acknowledge the following people from the University of Technology (UTS): My fellow students in the Innovation and Enterprise Research Laboratory, through countless discussions, they have provided me with insightful suggestions and valuable feedbacks that have helped enhance my work, special thanks also go to Dr. Benjamin Johnston who provided me with valuable feedback that enhanced the final version of this dissertation. QCIS people for their support. Special thanks go to all people at UTS who provided me with the needed support. The many researchers, including Dr. Mohammad Momani, Jamal and all other friends, whose kind suggestions, feedback and advice were invaluable in my research. Last, but not the least, I would like to thank my family for the endless support they provided me throughout my life. Without their love, patience and encouragements I would not have finished this thesis. First, I would like to thank my wife and best friend, Intisar, for supporting me and keeping me happy throughout my life until we achieved our mission towards our sons and this degree. I would like to thank my sons and their partners: Samer and Hanna, Rami and Marium, Fadi and Rawan for their love and support. Special thanks go to my daughters Hanna, Susan and Rawan who provided me with a continuous proof reading throughout my doctoral stint. I would also like to thank all my brothers and my sister for their support. Special thanks go to my brothers and friends: Ahmad, Yousef and v

7 Abu-Suhail, for giving me perspective whenever I needed it and all the numerous friends who have been there for me in times of need and helped me stay during the long, hard and ultimately enriching time that I have spent as a graduate student. In conclusion, I recognize that this research would not have been possible without the financial assistance from the University of Technology through the Australian Postgraduate Awards (APA), Faculty of Engineering and Information Technology (FEIT), Centre of Quantum Computation and Intelligent Systems (QCIS), and Innovation and Enterprise Research Laboratory; I express my gratitude to these agencies. vi

8 Table of Contents 1. Introduction Research Background and Related Work Credibility-Based Social Service Selection and Recommendation Experimentation and Evaluation - Recommendation Experimentation and Evaluation - Service Selection Mitigating Risk in Social Service Selection Conclusion and Future Work Bibliography vii

9 Extended Table of Contents Abstract... i List of Figures... xiii List of Tables... xvi List of Abbreviations... xviii Introduction Motivations Scenario Scenario Problem Scope and Definition Scope Limitations and Assumptions Significance Research Methodology Publications Arising From This Thesis Dissertation Outline Research Background and Related Work Terminology and Definitions Web Service Selection Overview of Key Technologies Semantic Web Services Web-based Social Networks Recommender Systems and Collaborative Based Filtering Web Service Selection and Recommendation Challenges Cold-Start Users Issue Sparsity Issue viii

10 Privacy Issue Scalability Capturing Users Preferences and Ratings Top Authors addressing Challenges Related Works Web-based Social Network Analysis Web-Based Social Networks and Trust Trust-Based Collaborative Filtering (CF) Follow the Leader Finding Expert Leaders in a Social Network Clustering and Collaborative Filtering Social Based Service Selection Reputation Based Approaches Recommender Based Approaches Referral Based Approaches Recommendation Approaches against Challenges Chapter Summary Credibility-Based Social Service Selection and Recommendation Introduction TECBF: Trustworthiness Expertise Credibility-Based Framework UCrM: User Credibility Model Trustworthiness Component Expertise Component Computing User Credibility Credibility Dynamism Clustering Users Based on Credibility Using Leaders as Potential Top-N Recommenders WSCrM: Web Service Credibility Model Web Service Credibility from Trustworthiness Web Service Credibility from Expertise Component Computing Web Service Credibility Web Service Credibility Dynamism ix

11 Web Service Credibility Characteristics Web Service Credibility Model - Flexibility and Robustness SSSRM: Social-based Service Selection and Recommendation Model Evaluation Framework Chapter Summary Experimentation and Evaluation - Recommendation Introduction Experiments Setup Datasets Selection Dataset Pre-Processing Preparation Datasets Summary Items not appearing in Leaders Scope Analysis WBSN Visualization The EPINIONS Subset Social Network Analysis Studio (SNAS) Cyclic Trust in SNAS Recommendation Prediction Using EPINIONS Subsets Benchmark Recommendation Prediction Algorithms Recommendation Prediction Evaluation Measures The EPINIONS Subsets Prediction Results Further Experiments on Large Datasets Test Options on Large Datasets Definitions Benchmark: MAE Comparison between Credibility and Trust Prediction Coverage Prediction Response Time Prediction Confidence Based on Average Number of Predictors WBSN Leaders Clustering and Identification Evaluation Centrality Measures for Three Datasets Interest Similarity Leaders Credibility Threshold Analysis Flexibility, Scalability and Applications x

12 4.7. Results Summary Conclusions Experimentation and Evaluation - Service Selection Social Service Selection Approach Motivation and Contributions Social Service Selection Related Works Notations and Definitions User Query Model Predicting Missing Quality Values Web Service Credibility Bootstrapping Benchmark Service Selection Algorithms Utility-Based Selection Approach Trustworthiness Based Selection Approach Experimental Evaluation Agent-based Modeling and Simulation (ABMS) Overview Simulation Model Evaluation Metrics Experiments and Simulation Results Validity of Web Service Credibility Computational Model Social Service Selection with User Domain Knowledge (UDK) Considerations Benchmarking Credibility-Based Approach with Utility-Based Approach Benchmarking Credibility Based Approach with Trustworthiness Based Approach Results Summary Conclusions Mitigating Risk in Social Service Selection Introduction Motivation and Contributions Related Works Trust and Risk in Service Selection xi

13 Mitigating Risk in Social Service Selection Perceived Risk and Risk Attitude in Web Service Selection Perceived Performance Risk in Web Service Selection Risk Attitude and Perceived Risk Customer Risk Attitude and Behavior Model Perceived Risk from Risk Attitude Perspective Service Selection with Risk Attitude and Perceived Risk Experimental Evaluation Simulation Model Validity and Dynamism of Perceived Risk Computation Model Risk-Based Social Service Selection Results Summary Summary and Conclusions Conclusion and Future Work Contributions Primary Contribution Secondary Contributions Scalability, Flexibility and Robustness Applications and Possible Commercialization Future Research Directions Concluding Remarks Bibliography xii

14 List of Figures Figure 2.1. Referral Graph [223] Figure 3.1. TECBF: Trustworthiness Expertise Credibility-Based Framework Figure 3.2. User Credibility Model (UCrM) in WBSN Figure 3.3. Direct and Indirect Followers Trust Figure 3.4. Impact of Decay Factor (λ) on Credibility Figure 3.5. Web service Credibility Model (WSCrM) Figure 3.6. Social Service Selection Based on UDK and Service Credibility Algorithm Figure 3.7. TECBF Evaluation framework Figure 4.1. Social Network Analysis Studio (SNAS) Graphical User interface Figure 4.2. Follow the Leader Model for Social Network in Item Figure 4.3. Social Network Relations in Item Figure 4.4. MAE Average for all Predictions over all Subsets Figure 4.5. Credibility Algorithm vs. Trust Algorithm Prediction for all Datasets Figure 4.6. Trust Algorithm Prediction % Coverage for all Datasets Figure 4.7. Trust Algorithm Prediction Coverage per Test Option for all Datasets Figure 4.8. Credibility Algorithm prediction time as % of Trust Algorithm time Figure 4.9. Leaders vs. Followers In-Degree and Average Ratings for all Datasets xiii

15 Figure Interest Similarity and Friends K12 Acting as Leaders - Ratio for all Datasets Figure 5.1. SNA Simulation Tool User Interface Figure 5.2. Impact of Trustworthiness and Expertise on WS Credibility for WS (S04) Figure 5.3. Malicious Web service behavior (Facebook Privacy Scenario) Figure 5.4. Social service selection Flowchart Algorithm Figure 5.5. Social Service Selection based on customer UDK and service credibility Follow the Leader Model Figure 5.6. Top-5 services credibility at last round Figure 5.7. S24 versus S12 during simulation session Figure 5.8. Social Service Selection with Credibility and UDK Follow the Leader Figure 5.9. Average number of candidate services returned from 99 services match: based on user preferences number (Utility-Based) Figure Number of candidate services versus varied user pref. number and QoS attributes (Utility-Based) Figure Top-M Candidates: Credibility-based versus Utilitybased Figure 6.1. User risk attitude and behavior Model Figure 6.2. Perceived risk variation with different risk attitudes Figure 6.3. Risk-based social service selection: Algorithm Figure 6.4. SNA Simulation Tool User Interface Figure 6.5. Expected utility based credibility vs. perceived risk for different risk attitudes RA = (1, 0.5, 0) Figure 6.6. Dynamism of expected utility and Perceived Risk for different Risk Attitudes, RA = (1, 0) from (WS=S05) xiv

16 Figure 6.7. Customers select Web service randomly Follow the leader Figure 6.8. Social Service Selection based on customer risk attitude and service credibility Follow the Leader Model xv

17 List of Tables Table 2.1: Recommendation approaches that address at least three challenges Table 2.2: Challenges addressed in recommendation approaches Table 3.1: User Credibility computation example Table 4.1: Datasets summary Table 4.2: Items not appearing in leaders scope analysis Table 4.3: Experimental results of prediction algorithms: cold-start Table 4.4: Experimental results of prediction algorithms: experienced users.. 96 Table 4.5: Detailed MAE comparison for all datasets: Credibility Algorithm vs. Trust Algorithm prediction for all test options Table 4.6: Credibility Algorithm vs. Trust Algorithm prediction for all datasets Table 4.7: Credibility average raters vs. Trust average raters Table 4.8: Leaders vs. followers centrality degree and average ratings for all datasets Table 4.9: Leaders vs. followers in-degree and average ratings for all datasets Table 4.10: EPINIONS dataset: Target user Interest Similarity measure with (leaders and friends) communities Table 4.11: All datasets: Target user Interest Similarity measure with (leaders and friends) communities Table 4.12: Credibility threshold analysis for EPINIONS dataset Table 5.1: List of home loan services Table 5.2: Top-5 services credibility at last round xvi

18 Table 5.3: R-Precision for credibility-based and utility-based models Table 5.4: User query QoS attributes template Table 6.1: Top-5 home loan services perceived risk as viewed from different users xvii

19 List of Abbreviations ABMS Agent-based Modeling and Simulation B2B Business-to-Business B2C Business-to-Consumer BPEL4WS Business Process Execution Language for Web Services CF Collaborative Filtering HITS Hyperlink-Induced Topic Search IOPE Input, Output, Post-conditions and Effects MAE Mean Absolute Error OWL Web Ontology Language P2P Peer to Peer QoS Quality of Service QoWS Quality of Web Service RDF Resource Description Framework RS Recommender System SLA Service Level Agreement SNA Social Network Analysis SNAS Social Network Analysis Studio SOA Service Oriented Architecture SOAP Simple Object Access Protocol SOC Service Oriented Computing SSSRM Social-based Service Selection and Recommendation Model SWS Semantic Web Services TECBF Trustworthiness Expertise Credibility-Based Framework UCrM User Credibility Model UDDI Universal Description, Discovery and Integration UDK User Domain Knowledge URI Uniform Resource Identifier xviii

20 W3C WBSN WS WSCrM WSDL WS-Policy XML World Wide Web Consortium Web-based Social Network Web Service Web Service Credibility Model Web Service Description Language Web Services Policy Framework Extensible Markup Language xix