The role of bias terms in binary recommender systems I realize that a recommender system applied to, for example, the Movielens dataset needs to account for bias. That is, one needs to adjust for the varying popularity of movies, and that users have different baselines for their ratings. If one users tends to rates movies high and another users rates movies low, a rating of 4 out of 5 implies different level of appreciation.
However, I struggle to understand the meaning of bias terms in a recommenders systems that uses binary data as its input. For example, when we instead of movie ratings have yes/no information on if a user purchased or viewed an item in an online store.
This implementation introduces scalar bias terms for both products and users. The method is a hybrid in the sense that it takes into account both user behaviour and metadata. The paper also describes how it is used on binary data in an online store. How should the bias terms be interpreted in this case?

Even if the system uses binary data as input (0/1), it can still have some "bias" (average, offset) of these zeros and ones. You can look at matrix factorization implementation here (for netflix competition data, which is very similar to movielens dataset):
https://github.com/pepe78/netflix_matrix_factorization
