Fr From a a Web eb Ser ervic vices es Catalo alog to a a Li - - PowerPoint PPT Presentation
Fr From a a Web eb Ser ervic vices es Catalo alog to a a Li Linked Ecosystem of f Se Services F. SLAIMI, S. SELLAMI, O.BOUCELMA AIX-MARSEILLE UNIVERSITY, FRANCE 1 Outline o Context and motivation o Related work o Graph construction o
AIX-MARSEILLE UNIVERSITY, FRANCE
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L Manual search of services and mashups is difficult
LServices ans mashups are sparse
à Tedious process of discovery and
recommendation
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Track Track Track Track Track link services/ mashups and users à discovery and recommendation
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Approaches Graphs Discovery Criteria Selection and recommendation
Operates on services, mashups, categories and social links between developers User profiles and preferences Linked Social Services [Maamar 2011] Based on Linked Data Principles Social links Trust based [Deng 2014] [Deng 2015] Based on common usage in mashups or by users
QoS evaluations
Trust Linked mashups [Bianchini 2014]
link between mashups of resources which is calculated based on the comparison of their terminological items Similarity
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(documentation, functional and non functional)
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Category : social Name : facebook Tags: social, webhooks summary: Social networking Category: social Name: twitter Tags: social, microblogging summary : Social microblogging
Facebook Twitter simS(facebook, twitter)=0,72
facebok twitter
Social
facebook twitter linkedin megaphone fonolo 0.7
0.72 0.6
0.72
à common services in mashups SimMashups (M1,M2)=
|"#$⋂"#&| |"#$∪"#&|
facebook SMS filckr Google maps
M1
facebook LinkedIn Google maps
M2 SimMashups (M1,M2)= &
( = 0,4
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S1 S2
S3 S4
S5
u3
u4 u1 u2
u3
u4
Similar interests
Track relation
Sim (ui,uj)=
|-./⋂|-.0| |-./|
Where Hui and Huj are the recent histories
Categories
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Services
S1
Mashups
M1
u1 u4 u3 u2 u5 u6 u7
Users
M2 Mn S2 Sn S3 C2 C3 Cn
…. …. ….
Similar Track Belongs to
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S3 S1 S4 S2 Sn S6 S5
C1 C2
Services relationships Users relationships
U1 U3 U2 U5 Un U6
M1
C1 C2
M2 M6 M7 M2
Mashups relationships
List of ranked services and mashups Services Discovery Services/mashups recommendation
User Request
U1
Sub graphs of services
User Watchlist
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User A Category: social Name: facebook User’s query
Service’s search
Recommended services Recommended mashups Services can be used with facebook
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Number of categories 116 Number of mashups 300 Number of services 700 Number of users (with wtachlists) 344
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0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 Top 5 Top 10 Precision Recall RMSE hit-rank
Recall, Precision, RMSE and Hit-rank numbers (w.r.t the number of recommended services)
0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 Top 5 Top 10 Precision Recall RMSE hit-rank
Recall, Precision, RMSE and Hit-rank numbers (w.r.t the number of recommended mashups) 19
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Approaches Precision @5 Precision @10 Recall @5 Recall @10 RMSE @5 RMSE @10 TrustSVD 0.73 0.75 0.61 0.63 0.211 0.2 WReG 0.80 0.85 0.70 0.74 0.2 0.185 Popular 0.41 0.39 0.34 0.61 0.31 0.3
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[Maamar 2011] Maamar, Z., Wives, L. K., Badr, Y., Elnaffar, S., Boukadi, K., Faci, N.: Linkedws: A novel web services discovery model based on the metaphor of Social networks. Simulation Modelling Practice and Theory, vol.19 (2011) 121-132 [Guo 2015] Guo, G., Zhang, J., Yorke-Smith., N.: TrustSVD: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, (2015) 123-129 [Deng 2015] Deng, S., Huang, L., Yin, Y., Tang, W.: Trust-based service recommendation in social
[Deng 2014] Deng, S., Huang, L. Xu, G.: Social network-based service recommendation with trust
[Bianchini 2014] Bianchini,D., Antonellis, V. D., Melchiori, M.: Link-Based Viewing of Multiple Web API
2014, Munich, Germany, (2014) 362–376
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