  In feeds recommendation, the first step is candidate generation. Most of the
candidate generation models are based on CTR estimation, which do not consider
user's satisfaction with the clicked item. Items with low quality but
attractive title (i.e., click baits) may be recommended to the user, which
worsens the user experience. One solution to this problem is to model the click
and the reading duration simultaneously under the multi-task learning (MTL)
framework. There are two challenges in the modeling. The first one is how to
deal with the zero duration of the negative samples, which does not necessarily
indicate dislikes. The second one is how to perform multi-task learning in the
candidate generation model with double tower structure that can only model one
single task. In this paper, we propose an distillation based multi-task
learning (DMTL) approach to tackle these two challenges. We model duration by
considering its dependency of click in the MTL, and then transfer the knowledge
learned from the MTL teacher model to the student candidate generation model by
distillation. Experiments conducted on dataset gathered from traffic logs of
Tencent Kandian's recommender system show that the proposed approach
outperforms the competitors significantly in modeling duration, which
demonstrates the effectiveness of the proposed candidate generation model.
