  Recommendation for new users, also called user cold start, has been a
well-recognized challenge for online recommender systems. Most existing methods
view the crux as the lack of initial data. However, in this paper, we argue
that there are neglected problems: 1) New users' behaviour follows much
different distributions from regular users. 2) Although personalized features
are involved, heavily imbalanced samples prevent the model from balancing
new/regular user distributions, as if the personalized features are
overwhelmed. We name the problem as the "submergence" of personalization. To
tackle this problem, we propose a novel module: Personalized COld Start MOdules
(POSO). Considering from a model architecture perspective, POSO personalizes
existing modules by introducing multiple user-group-specialized sub-modules.
Then, it fuses their outputs by personalized gates, resulting in comprehensive
representations. In such way, POSO projects imbalanced features to even
modules. POSO can be flexibly integrated into many existing modules and
effectively improves their performance with negligible computational overheads.
The proposed method shows remarkable advantage in industrial scenario. It has
been deployed on the large-scale recommender system of Kwai, and improves new
user Watch Time by a large margin (+7.75%). Moreover, POSO can be further
generalized to regular users, inactive users and returning users (+2%-3% on
Watch Time), as well as item cold start (+3.8% on Watch Time). Its
effectiveness has also been verified on public dataset (MovieLens 20M). We
believe such practical experience can be well generalized to other scenarios.
