  Waterfall Recommender System (RS), a popular form of RS in mobile
applications, is a stream of recommended items consisting of successive pages
that can be browsed by scrolling. In waterfall RS, when a user finishes
browsing a page, the edge (e.g., mobile phones) would send a request to the
cloud server to get a new page of recommendations, known as the paging request
mechanism. RSs typically put a large number of items into one page to reduce
excessive resource consumption from numerous paging requests, which, however,
would diminish the RSs' ability to timely renew the recommendations according
to users' real-time interest and lead to a poor user experience. Intuitively,
inserting additional requests inside pages to update the recommendations with a
higher frequency can alleviate the problem. However, previous attempts,
including only non-adaptive strategies (e.g., insert requests uniformly), would
eventually lead to resource overconsumption. To this end, we envision a new
learning task of edge intelligence named Intelligent Request Strategy Design
(IRSD). It aims to improve the effectiveness of waterfall RSs by determining
the appropriate occasions of request insertion based on users' real-time
intention. Moreover, we propose a new paradigm of adaptive request insertion
strategy named Uplift-based On-edge Smart Request Framework (AdaRequest).
AdaRequest 1) captures the dynamic change of users' intentions by matching
their real-time behaviors with their historical interests based on
attention-based neural networks. 2) estimates the counterfactual uplift of user
purchase brought by an inserted request based on causal inference. 3)
determines the final request insertion strategy by maximizing the utility
function under online resource constraints. We conduct extensive experiments on
both offline dataset and online A/B test to verify the effectiveness of
AdaRequest.
