I am building a recommender system. I have a list that shows me what a user has disliked and I use it to create a dataset. The dataset shows me: ProductID, Likes, Disklikes where the values are counts from other users that have disliked the same products. What are some of the algorithmic approaches I can use from this base? From this data I know I can get a probability. How would I also get a weighted average? Maybe some sort of Bayesian filtering approach is called for in these scenarios? 
Recommender system based on binary likes/disklikes?
recommender system;naive bayes classifier