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.
翻译:降水建议系统(RS)是移动应用中受欢迎的塞族共和国的流行形式,是一个建议项目流,由连续的页面组成,可以通过滚动浏览浏览浏览。在瀑布RS,当用户完成浏览页面时,边缘(例如移动电话)会向云端服务器发送请求,以获得新的一页建议,称为调用请求机制。塞族共和国通常将大量项目放入一页,以减少许多调用请求产生的过度资源消耗,然而,这将削弱塞族共和国根据用户实时兴趣及时更新建议的能力,导致用户经验差。在瀑布中,当用户完成浏览页面时,边缘(例如移动电话)会向云端服务器发送请求,以获得新的一页建议,称为调用请求机制。为此,我们设想了一个新的精锐智能智能智能搜索战略设计(IRSDD),目的是通过确定内向下流的内流效率,在页内插入更多内容更新建议更新建议,同时根据智能指令,在智能指令上,根据智能指令,在智能指令上,在智能指令上,根据智能指令上,在智能指令上,在上,根据智能指令上,在上,在上,根据智能指令上,在上,在上,在上,在上,在上,在上,根据智能指令上, 动作上, 动作上, 动作上,在上,根据智能指令上, 动作上, 动作上, 动作上, 动作上, 动作上,在上, 动作, 动作, 动作, 动作,根据智能,根据智能, 动作, 动作,根据智能, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作,根据智能, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作, 动作,