Outline ■Background >Why do those Web services need to be recommended? >What's the challenge? Context-aware Recommendation >What kind of context information can we use? >Basic method Context-aware feature learning >User context-aware features learning >Service service-aware features learning ■Experiment >Experimental Result >Performance Comparison ■Reference 2017/615 XDU ZJU
Background ➢Why do those Web services need to be recommended? ➢What’s the challenge? Context-aware Recommendation ➢What kind of context information can we use? ➢Basic method Context-aware feature learning ➢User context-aware features learning ➢Service service-aware features learning Experiment ➢ Experimental Result ➢ Performance Comparison Reference Outline 2017/6/15 XDU & ZJU 2
Background Generdestricted Web Service WSDL(XML) Interface SOAP >A Web service is a self-describing programmable application used to achieve interoperability and accessibility over a network 'Services Computing;Zhang,L.et WS=WSDL+Interface+SOAP Popularity >Amazon Relational Database Service >Amazon Simple Storage Service R D 如果请求中包含了P以证授权态数，则返回值中包含当前爱权用户的对于这本书裙收#的k。关于AP似 证须权的信息请参阅P爱枫说明 ■ Other similar component Thttp://api.doubancom/baok/subjeet/(subje They are almost >Open API: the same 他可以涌过s0n支并10位、13位，而且支持在其中 Douban,Sina Weibo,RenRe Thttp://spi.douban.com/bk/subjee/isn/[isbaID] -Amazon,Facebook,MySpac 20171615 XDU ZJU GET http://spi.douban.com/book/subjeet/isbn/9T87543639133
Background Web Service ➢ A Web service is a self-describing programmable application used to achieve interoperability and accessibility over a network ~ ‘Services Computing’, Zhang, L. etc. 2017/6/15 3 Other similar component ➢ Open API: − Douban, Sina Weibo, RenRen, 51.com − Amazon, Facebook, MySpace, Twitter, eBay, Google Maps WSDL(XML) Interface SOAP WS=WSDL+Interface+SOAP Popularity ➢ Amazon Relational Database Service ➢ Amazon Simple Storage Service etc. They are almost the same Generalrestricted XDU & ZJU
Background ■ Backgroud Amazon Relational Database Service .t awsdocumentation >Cloud Computing>the number of Web services exploding elational Amazon RDS Best Practices maron ged with ■Decision Basis Amazon Simple Storage Service nmnlog >QoS(response time,throughput,availability et MySQL an Welcome to Amazon S3 Problem Oracle on >Everyone wants to invoke the one whose gos is the best. DB Instance >Is there a service suitable for everyone?No.>WhContext Service uery AP >So the real problem is which one should I invoke?obrton o Budkets SOAP API aries Operations on Objects ·Amazon S3 Resources e Command History Personalization Prediction tory 2017/615 XDU ZJU
Background Backgroud ➢ Cloud Computing the number of Web services is exploding 2017/6/15 4 Amazon Relational Database Service Amazon Simple Storage Service Problem ➢ Everyone wants to invoke the one whose qos is the best. ➢ Is there a service suitable for everyone? No. Why? Context ➢ So the real problem is : which one should I invoke? Personalization & Prediction Decision Basis ➢ QoS(response time, throughput, availability etc.) XDU & ZJU
Background Formalization of the Problem Similar with rating prediction >Personalized QoS prediction in e-commerce systems User-Service Invocation Matrix Sufficient service1 service2 service3 service4 million Features user1 911 关 年 然 + user2 法 q22 然 益 Effective user3 关 关 关 关 Model user4 q41 关 关 q44 Cold-Start user5 关 q53 Problem → ■ Challenge million >Data Sparsity>Cold Start Problem >Large Scale Feasibility 20171615 XDU ZJU
Background Formalization of the Problem ➢ Personalized QoS prediction ➢ User-Service Invocation Matrix 2017/6/15 5 service1 service2 service3 service4 user1 q11 user2 q22 user3 user4 q41 q44 user5 q53 ➢ Large Scale Feasibility Challenge ➢ Data Sparsity Cold Start Problem million million Sufficient Features + Effective Model Cold-Start Problem Similar with rating prediction in e-commerce systems XDU & ZJU
Context-aware recommendation Contextual Features Selection Basis:factors dominating QoS:physical configuration Service CPU,Memory, Service Provider Bandwidth etc. User Network Bandwidth etc. Connection etc. Feature Quantification >User-User:Geographical Distance Service-Service:Service Provider →Why? 2017/1615 XDU ZJU
Context-aware recommendation Contextual Features Selection ➢ Basis: factors dominating QoS: physical configuration 2017/6/15 6 Service User Service Provider Network Connection etc. CPU, Memory, Bandwidth etc. Bandwidth etc. Feature Quantification ➢ User-User: Geographical Distance ➢ Service-Service: Service Provider Why? XDU & ZJU