Recommending new products using k-means clusters?

I'm trying to figure out the best way to recommend images based on past classifications using k-means clusters. What I have done is mapped the RGB values of a set of images, performed a k-means cluster analysis on those RGB values, and attached a "rating" to each image. This has created Voronoi cells similar to this graph. I've stored the cluster centers and ratings into my "training set".

The next step is to take a new image and make a recommendation based on the training set previous images. I'm not sure how to proceed. Would I want to implement a Collaborative Filtering process? Or do I need to perform more processing on the data?

Not sure if it matters but I'm using Apache Spark for the project. Thanks!

Edit: Collaborative filtering is probably not the best way to proceed, since the features being compared for products uses more than just ratings. I need to compare the similarity. I'm guessing this would involve heavy matrix operations?