  Introducing consumed items as users' implicit feedback in matrix
factorization (MF) method, SVD++ is one of the most effective collaborative
filtering methods for personalized recommender systems. Though powerful, SVD++
has two limitations: (i). only user-side implicit feedback is utilized, whereas
item-side implicit feedback, which can also enrich item representations, is not
leveraged;(ii). in SVD++, the interacted items are equally weighted when
combining the implicit feedback, which can not reflect user's true preferences
accurately. To tackle the above limitations, in this paper we propose
Graph-based collaborative filtering (GCF) model, Weighted Graph-based
collaborative filtering (W-GCF) model and Attentive Graph-based collaborative
filtering (A-GCF) model, which (i). generalize the implicit feedback to item
side based on the user-item bipartite graph; (ii). flexibly learn the weights
of individuals in the implicit feedback hence improve the model's capacity.
Comprehensive experiments show that our proposed models outperform
state-of-the-art models.For sparse implicit feedback scenarios, additional
improvement is further achieved by leveraging the step-two implicit feedback
information.
