  A large-scale recommender system usually consists of recall and ranking
modules. The goal of ranking modules (aka rankers) is to elaborately
discriminate users' preference on item candidates proposed by recall modules.
With the success of deep learning techniques in various domains, we have
witnessed the mainstream rankers evolve from traditional models to deep neural
models. However, the way that we design and use rankers remains unchanged:
offline training the model, freezing the parameters, and deploying it for
online serving. Actually, the candidate items are determined by specific user
requests, in which underlying distributions (e.g., the proportion of items for
different categories, the proportion of popular or new items) are highly
different from one another in a production environment. The classical
parameter-frozen inference manner cannot adapt to dynamic serving
circumstances, making rankers' performance compromised. In this paper, we
propose a new training and inference paradigm, termed as Ada-Ranker, to address
the challenges of dynamic online serving. Instead of using parameter-frozen
models for universal serving, Ada-Ranker can adaptively modulate parameters of
a ranker according to the data distribution of the current group of item
candidates. We first extract distribution patterns from the item candidates.
Then, we modulate the ranker by the patterns to make the ranker adapt to the
current data distribution. Finally, we use the revised ranker to score the
candidate list. In this way, we empower the ranker with the capacity of
adapting from a global model to a local model which better handles the current
task.
