Recently, rating prediction algorithms that are based on matrix factorization have become increasingly popular, due to their high accuracy and scalability (Koren, Bell, and Volinsky 2009).
, 2010: where X X X −di and Z Z Z −di are all the author and topic assignments, respectively, excluding the assignment for the i-th word of the d-th document; c (AT) at is the count of topic t assignments to author a; and c is the count of word w di in author topic t. Here, all the counts exclude the di-th assignments (i.e., x di and z di ).