  Matching module plays a critical role in display advertising systems. Without
query from user, it is challenging for system to match user traffic and ads
suitably. System packs up a group of users with common properties such as the
same gender or similar shopping interests into a crowd. Here term crowd can be
viewed as a tag over users. Then advertisers bid for different crowds and
deliver their ads to those targeted users. Matching module in most industrial
display advertising systems follows a two-stage paradigm. When receiving a user
request, matching system (i) finds the crowds that the user belongs to; (ii)
retrieves all ads that have targeted those crowds. However, in applications
such as display advertising at Alibaba, with very large volumes of crowds and
ads, both stages of matching have to truncate the long-tailed parts for online
serving, under limited latency. That's to say, not all ads have the chance to
participate in online matching. This results in sub-optimal result for both
advertising performance and platform revenue. In this paper, we study the
truncation problem and propose a Truncation Free Matching System (TFMS). The
basic idea is to decouple the matching computation from the online pipeline.
Instead of executing the two-stage matching when user visits, TFMS utilizes a
near-line truncation-free matching to pre-calculate and store those top
valuable ads for each user. Then the online pipeline just needs to fetch the
pre-stored ads as matching results. In this way, we can jump out of online
system's latency and computation cost limitations, and leverage flexible
computation resource to finish the user-ad matching. TFMS has been deployed in
our productive system since 2019, bringing (i) more than 50% improvement of
impressions for advertisers who encountered truncation before, (ii) 9.4%
Revenue Per Mile gain, which is significant enough for the business.
