Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation

Algorithm 1

Framework of MOEA-EPG.

 Input the training data, the test data, and a binary matrix R

 Set g=0, L=10, N=100, T=3000, K=0.5

 Cluster all the users U in the training data into four clusters with size ,

  and

 Set with

 for j=1 to 4

   Compute the similarities of different users in based on Eq. (7)

   Set

   for k=1 to

      Compute the available items of user , as recorded by the set

      for  =1 to Ns

       Compute the predict rating for user to item by Eq. (8)

    EPOP = Initialization(L, N, R, pr)

    while g T

        Epa = Objective_Func(EPOP)

        Epa = Fast_Non_Dominated_Sort(EPa)

        EPOP = Selection(EPOP, Epa)

        EPOP = Uniform_Crossover(EPOP)

        NPOP = Mutation(EPOP)

        POP =

        EPOP = Update(POP, Epa, N)

        g = g+1

    = EPOP

 Return P