Intelligent Request Strategy Design in Recommender System
Xufeng Qian, Yue Xu, Fuyu Lv, Shengyu Zhang, Ziwen Jiang, Qingwen Liu,, Xiaoyi Zeng, Tat-Seng Chua, Fei Wu

TL;DR
This paper introduces AdaRequest, an adaptive request insertion framework for waterfall recommender systems that dynamically balances recommendation freshness and resource use by modeling user intent and estimating uplift through neural networks and causal inference.
Contribution
It proposes a novel learning task called Intelligent Request Strategy Design and develops AdaRequest, an adaptive framework that improves recommendation relevance while managing resource constraints.
Findings
AdaRequest outperforms non-adaptive strategies in offline and online tests.
It effectively captures user intent changes with attention-based neural networks.
The framework optimizes request timing to enhance user engagement.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRecommender Systems and Techniques · IoT and Edge/Fog Computing · Personal Information Management and User Behavior
MethodsTest
