I want to create a recommendation system using link Prediction with ecommerce data, where users and items are represented as nodes, and interactions like click, cart, like, and buy are represented as edges. The reference I used is available at here
My goal is to train a link prediction model that predicts interactions (i.e., like, buy) between users and items that have not previously occurred.
Following the tutorial mentioned above, I divided my data into a positive graph and a negative graph and applied them to model, obtaining positive scores and negative scores. I then trained the model using hinge loss. While training the model using this approach was straightforward, I’m unsure about how to utilize the trained model effectively.
I have the following questions:

Should I select the top k recommended items based on the Positive Score?
I believe using positive scores is not aligned with my goal, as the positive graph consists of interactions that have already occurred between users and items. Using positive scores doesn’t seem appropriate. On the other hand, relying on negative scores also doesn’t seem suitable. 
Is it possible to use Positive Scores and Negative Scores to predict interactions that haven’t occurred before, which is my goal? If it’s possible, I would appreciate guidance on how to approach this effectively.
Thanks for the help