According to Mitchell:
“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T , as measured by P , improves with experience E” (Mitchell, 1997) .
If we put this in terms of recommender systems:
Task: recommend products
Experience: includes the experience of observing a set of examples encoded in a ratings matrix
Performance: metric i.e. rmse between real and predicted ratings
Is my approach okay?
Any suggestions are welcomed!