论文标题
如何将用户控制在联合的Top-N推荐中，以学习排名
How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank
论文作者
论文摘要
推荐服务在几个以用户为中心的应用程序中广泛采用，作为减轻信息超载问题并帮助用户在可能选择的广阔空间中定向启动的一种工具。在这种情况下，数据所有权是一个至关重要的问题，因为用户可能不愿意与中央服务器共享其敏感偏好（例如，访问的位置）。不幸的是，数据收集和收集是基于现代，最先进的推荐方法。为了解决这个问题，我们提出了FPL，这是一种体系结构，在该体系结构中，在控制离开其设备的敏感数据量的同时，在培训中心分解模型中协作。所提出的方法通过遵循联合学习原则来实现成对的学习到级别优化，最初是为了减轻传统机器学习的隐私风险。公共实施可在https://split.to/sisinflab-fpl上获得。
Recommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a vast space of possible choices. In such scenarios, data ownership is a crucial concern since users may not be willing to share their sensitive preferences (e.g., visited locations) with a central server. Unfortunately, data harvesting and collection is at the basis of modern, state-of-the-art approaches to recommendation. To address this issue, we present FPL, an architecture in which users collaborate in training a central factorization model while controlling the amount of sensitive data leaving their devices. The proposed approach implements pair-wise learning-to-rank optimization by following the Federated Learning principles, originally conceived to mitigate the privacy risks of traditional machine learning. The public implementation is available at https://split.to/sisinflab-fpl.
