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, privacy is a crucial concern since users may not be willing to share their sensitive preferences (e.g., visited locations, read books, bought items) with a central server. Unfortunately, data harvesting and collection is at the basis of modern, state-of-the-art approaches to recommendation. Decreased users' willingness to share personal information along with data minimization/protection policies (such as the European GDPR), can result in the "data scarcity" dilemma affecting data-intensive applications such as recommender systems (RS). We argue that scarcity of adequate data due to privacy concerns can severely impair the quality of learned models and, in the long term, result in a turnover and disloyal customers with direct consequences for lives, society, and businesses. To address these issues, 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 conceived originally to mitigate the privacy risks of traditional machine learning. We have conducted an extensive experimental evaluation on three Foursquare datasets and have verified the effectiveness of the proposed architecture concerning accuracy and beyond-accuracy objectives. We have analyzed the impact of communication cost with the central server on the system's performance, by varying the amount of local computation and training parallelism. Finally, we have carefully examined the impact of disclosed users' information on the quality of the final model and ...
翻译:一些以用户为中心的应用程序广泛采用建议服务,作为缓解信息超载问题和帮助用户在可能的选择的广阔空间里熟悉信息的工具。在这种情况下,隐私是一个关键问题,因为用户可能不愿意与中央服务器分享其敏感偏好(例如访问地点、阅读书籍、购买物品),不幸的是,数据收集和收集是现代、最新的建议方法的基础。用户在分享个人信息和数据最小化/保护政策(如欧洲GDPR)的同时分享数据最小化/保护政策(如欧洲GDPR)的意愿降低,这可能导致“数据稀缺”的困境影响数据密集应用,例如建议系统(RS)等。我们认为,由于对隐私的关切而缺乏足够的数据,可能严重损害学习到的模型的质量,从长远来看,导致顾客更替和不忠,直接影响到生活、社会和企业。为了解决这些问题,我们提出了FPL, 用户在培训中央因素化模型中进行合作,同时控制敏感数据离开设备的数量。 拟议的方法通过对等级的优化进行双向学习,通过遵循先进的质量系统(RS)的精确性评估,我们最初对四个系统进行了成本评估,我们提出了关于保密性分析,我们进行了关于核心数据的系统评估。