4 with respect to µ i and set it to zero, we obtain, which implies that the MLE of µ i is the sample mean.
since we are dealing with a set of weighted samples, the sample mean is replaced by the weighted average (the weights w l are assumed to add up to one): Our model yields an intuitive result: to estimate the mean vector µ i of u i , we can simply take the weighted average of the latent factors u l from the nearest-neighbors as an estimator, where the weights are the similarity scores between the textual proles of user i and its neighbors.