Improving Similarity$-$based Recommendation System by tuning its hyperparameters

Below, we will be tuning hyperparameters for the KNNBasic algorithm. Let's try to understand some of the hyperparameters of the KNNBasic algorithm:

k $($int$)$ The $($max$)$ number of neighbors to take into account for aggregation. Default is $40.$

min$\_$k $($int$)$ The minimum number of neighbors to take into account for aggregation. If there are not enough neighbors, the prediction is set to the global mean of all ratings. Default is $1.$

sim$\_$options $($dict$)$ A dictionary of options for the similarity measure. And there are four similarity measures available in surprise $-$

cosinemsd $($default$)$PearsonPearson baseline