  Feed recommendation models are widely adopted by numerous feed platforms to
encourage users to explore the contents they are interested in. However, most
of the current research simply focus on targeting user's preference and lack
in-depth study of avoiding objectionable contents to be frequently recommended,
which is a common reason that let user detest. To address this issue, we
propose a Deep Latent Emotion Network (DLEN) model to extract latent
probability of a user preferring a feed by modeling multiple targets with
semi-supervised learning. With this method, the conflicts of different targets
are successfully reduced in the training phase, which improves the training
accuracy of each target effectively. Besides, by adding this latent state of
user emotion to multi-target fusion, the model is capable of decreasing the
probability to recommend objectionable contents to improve user retention and
stay time during online testing phase. DLEN is deployed on a real-world
multi-task feed recommendation scenario of Tencent QQ-Small-World with a
dataset containing over a billion samples, and it exhibits a significant
performance advantage over the SOTA MTL model in offline evaluation, together
with a considerable increase by 3.02% in view-count and 2.63% in user stay-time
in production. Complementary offline experiments of DLEN model on a public
dataset also repeat improvements in various scenarios. At present, DLEN model
has been successfully deployed in Tencent's feed recommendation system.
