Context-aware recommendation ■ Probabilistic Matrix Factorization >MF can factorize the high-rank user-service invocation feature space into the joint low-rank feature space >Q={q):mXn user-service invocation matrix >UERd*m,SERd*n:user and service feature matrices US,Inner product of two d-rank feature vectors Indicator function(0/1) Shinimizaton functon i=1=1 Regularization Term How can the context information be merged into the m-2空4aEN阳 basic model? Objective Optimization Function 20171615 XDU ZJU Context-aware recommendation  Probabilistic Matrix Factorization ➢ MF can factorize the high-rank user-service invocation feature space into the joint low-rank feature space ➢ Q={qij}: m×n user-service invocation matrix ➢ U∈Rdm, S∈Rdn : user and service feature matrices 2017/6/15 11 j T qij Ui S    m i n j j T ij ij i U S I q U S 1 1 2 , ( ) 2 1 min 2 2 1 1 2 2 2 ( ) 2 1 min F S F U m i n j j T L I ij qij Ui S U S          Inner product of two d-rank feature vectors Minimization function Objective Optimization Function Indicator function(0/1) How can the context information be merged into the basic model? Regularization Term XDU & ZJU