R: GLMNET odd behavior when model is reran

I am trying to use LASSO for variable selection, and attempted the implementation in R using the

package. This is the code I wrote so far:

 set.seed(1)

 library(glmnet)

 return =  matrix(ret.ff.zoo[which(index(ret.ff.zoo) == beta.df$date[1]),])

 data = matrix(unlist(beta.df[which(beta.df$date == beta.df$date[1]),][,-1]), ncol = num.factors)

 dimnames(data)[[2]] <- names(beta.df)[-1]

 model <- cv.glmnet(data, return, standardize = TRUE)

 coef(model)

This is what I obtain when I run it the first time:

 > coef(model)

 15 x 1 sparse Matrix of class "dgCMatrix"

                       1

 (Intercept) 0.009159452

 VAL         .          

 EQ          .          

 EFF         .          

 SIZE        0.018479078

 MOM         .          

 FSCR        .          

 MSCR        .          

 SY          .          

 URP         .          

 UMP         .          

 UNIF        .          

 OIL         .          

 DEI         .          

 PROD        .             

BUT, this is what I obtain when I run the SAME code once more:

 > coef(model)

 15 x 1 sparse Matrix of class "dgCMatrix"

                       1

 (Intercept) 0.008031915

 VAL         .          

 EQ          .          

 EFF         .          

 SIZE        0.021250778

 MOM         .          

 FSCR        .          

 MSCR        .          

 SY          .          

 URP         .          

 UMP         .          

 UNIF        .          

 OIL         .          

 DEI         .          

 PROD        .          

I am not sure why the model behaves this way. How would I be able to choose a final model if the coefficients change at every run? Does it use a different tuning parameter $\lambda$ at every run? I thought that uses by default?!

I have just started learning about this package, and would appreciate any help I can get!

Thank you!