  The goal of recommender systems is to help users find useful items from a
large catalog of items by producing a list of item recommendations for every
user. Data sets based on implicit data collection have a number of special
characteristics. The user and item interaction matrix is often complete, i.e.
every user and item pair has an interaction value or zero for no interaction,
and the goal is to rank the items for every user. This study presents a simple
new algorithm for implicit data that matches or outperforms baselines in
accuracy. The algorithm can be motivated intuitively by the Multivariate Normal
Distribution (MVN), where have a closed form expression for the ranking of
non-interactions given user's interactions. The main difference to kNN and SVD
baselines is that predictions are carried out using only the known
interactions. Modified baselines with this trick have a better accuracy,
however it also results in simpler models with fewer hyperparameters for
implicit data. Our results suggest that this idea should used in Top-N
recommendation with small seed sizes and the MVN is a simple way to do so.
