Context-aware recommendation Feature Quantification (Con.) >Observation Just the same with browsing in -For the exactly same service or user: Internet city,town,community Users in different locations usually experience different QoS Users in the same location usually experience similar QoS Services operated by different providers usually offer different QoS Services operated by the same providers usually offer similar QoS ■Assumption: ■Reasonable? >Users located nearly with each other have similar IT infrastructure >Services provided by the same company have similar physica configuration 201716115 XDU ZJU
Context-aware recommendation Feature Quantification (Con.) ➢ Observation – For the exactly same service or user: 2017/6/15 7 Assumption: ➢Users located nearly with each other have similar IT infrastructure ➢Services provided by the same company have similar physical configuration ✓ Users in different locations usually experience different QoS ✓ Users in the same location usually experience similar QoS city, town, community Just the same with browsing in Internet ✓ Services operated by different providers usually offer different QoS ✓ Services operated by the same providers usually offer similar QoS Reasonable? XDU & ZJU
Context-aware recommendation The State of Assumption:Reasonable? the Internet Private Cloud Seoul shorter response time Standard Reserved Instances Comapratively Small (Defaut) Reasonable Large Et灯e Large ration Standard Re Extra Large s517 50.299 per Hour Doubie Extra Large 1034 longer response EC2 Company 3 time Beijing Micro Reserved Instances 52 $0.012 per Hour Web service invocation scenario Extra Large 5272 $0.169 per Hour Doubla Extra Large 5544 $0.338 par Hour Quadruple Extra Large 51098 $0.676 per Hour High-CPU Reserved Instances 5161 09 per Hour Extra Large 544 $0.36 per Hour Cluster Compute Reserved Instances Algorithm:the basic model Quadruple Extra Large N/A N/A Eight Extra Large 1762 50.904 per Hour High-Memory Cluster Reserved Instances >Collaborative Filtering/Matrix Factorization traditiona recommender system 2017/615 XDU ZJU
Context-aware recommendation Assumption: Reasonable? 2017/6/15 8 Comapratively Reasonable Web service invocation scenario Algorithm: the basic model ➢ Collaborative Filtering/Matrix Factorization traditional recommender system XDU & ZJU
Context-aware recommendation ■Related Work→Collabor Similar Service S: Invocation U >Explore similar users and ser Experience U 924 invocation records much like recomm U Whose invocation is similar with mine? >Pearson Correlation Coefficient Similarity Computation Sim(a,w0）= ∑(gn-a,g。-) ∑e(9。-q,gy-4,） V∑g.-.}V∑9.-了 5m-2.-a.a,- ■Predicted Results Memory-based & >Self Average Weighted Average Heuristic Pu=u+ ∑est Sim(.,XL,-a) ∑eo Sim,4X.- ∑ Pu=i+ Sim(u,ua) ∑esSim(,i.） 20171615 XDU ZJU
Related Work Collaborative Filtering ➢ Explore similar users and services through their historical invocation records much like recommender systems Context-aware recommendation 2017/6/15 9 Similar Service Invocation Experience Whose invocation is similar with mine? ➢ Pearson Correlation Coefficient Similarity Computation i I ui u i I ai a i I ai a ui u q q q q q q q q Sim a u 2 2 ( ) ( ) ( )( ) ( , ) U u U uj j u ui i u U ui i uj j q q q q q q q q Sim i j 2 2 ( ) ( ) ( )( ) ( , ) Predicted Results ➢ Self Average + Weighted Average ( ) ( ) ( , ) ( , )( ) u S a a u S u a u a ui a a a Sim u u Sim u u r u p u ( ) ( ) ( , ) ( , )( ) i S i k i S i k i k ui k k k Sim i i Sim i i r i p i Memory-based & Heuristic XDU & ZJU