  Over the past decades, for One-Class Collaborative Filtering (OCCF), many
learning objectives have been researched based on a variety of underlying
probabilistic models. From our analysis, we observe that models trained with
different OCCF objectives capture distinct aspects of user-item relationships,
which in turn produces complementary recommendations. This paper proposes a
novel OCCF framework, named ConCF, that exploits the complementarity from
heterogeneous objectives throughout the training process, generating a more
generalizable model. ConCF constructs a multi-branch variant of a given target
model by adding auxiliary heads, each of which is trained with heterogeneous
objectives. Then, it generates consensus by consolidating the various views
from the heads, and guides the heads based on the consensus. The heads are
collaboratively evolved based on their complementarity throughout the training,
which again results in generating more accurate consensus iteratively. After
training, we convert the multi-branch architecture back to the original target
model by removing the auxiliary heads, thus there is no extra inference cost
for the deployment. Our extensive experiments on real-world datasets
demonstrate that ConCF significantly improves the generalization of the model
by exploiting the complementarity from heterogeneous objectives.
