(2009) proposed a matrix factorization method based on SVD to learn latent representations of users and items from the rating matrix between users and items.
since the numbers of users and items in online platforms are usually huge, and the rating matrix between users and items is usually very sparse, it is quite diffi- cult for those rating based recommendation methods to learn accurate user and item representations (Zheng et al., 2017;Tay et al., 2018).