  Recommendation system serves as a conduit connecting users to an incredibly
large, diverse and ever growing collection of contents. In practice, missing
information on fresh (and tail) contents needs to be filled in order for them
to be exposed and discovered by their audience. We here share our success
stories in building a dedicated fresh content recommendation stack on a large
commercial platform. To nominate fresh contents, we built a multi-funnel
nomination system that combines (i) a two-tower model with strong
generalization power for coverage, and (ii) a sequence model with near
real-time update on user feedback for relevance. The multi-funnel setup
effectively balances between coverage and relevance. An in-depth study uncovers
the relationship between user activity level and their proximity toward fresh
contents, which further motivates a contextual multi-funnel setup. Nominated
fresh candidates are then scored and ranked by systems considering prediction
uncertainty to further bootstrap content with less exposure. We evaluate the
benefits of the dedicated fresh content recommendation stack, and the
multi-funnel nomination system in particular, through user corpus co-diverted
live experiments. We conduct multiple rounds of live experiments on a
commercial platform serving billion of users demonstrating efficacy of our
proposed methods.
