  Click-through rate (CTR) and post-click conversion rate (CVR) predictions are
two fundamental modules in industrial ranking systems such as recommender
systems, advertising, and search engines. Since CVR involves much fewer samples
than CTR (known as the CVR data sparsity problem), most of the existing works
try to leverage CTR&CVR multi-task learning to improve CVR performance.
However, typical coarse-grained sub-network/layer sharing methods may introduce
conflicts and lead to performance degradation, since not every neuron or neuron
connection in one layer should be shared between CVR and CTR tasks. This is
because users may have different fine-grained content feature preferences
between deep consumption and click behavior, represented by CVR and CTR,
respectively. To address this sharing&conflict problem, we propose a novel
multi-task CVR modeling scheme with neuron-connection level sharing named
NCS4CVR, which can automatically and flexibly learn which neuron weights are
shared or not shared without artificial experience. Compared with previous
layer-level sharing methods, this is the first time that a fine-grained CTR&CVR
sharing method at the neuron connection level is proposed, which is a research
paradigm shift in the sharing level. Both offline and online experiments
demonstrate that our method outperforms both the single-task model and the
layer-level sharing model. Our proposed method has now been successfully
deployed in an industry video recommender system serving major traffic.
