标题： 基于去中心化矩阵分解的隐私保护兴趣点推荐 显示英文标题

标题： Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization

摘要： 兴趣点（POI）推荐近年来由于基于位置的网络（例如Foursquare和Yelp）的日益流行而受到广泛关注。在现有的POI推荐方法中，基于矩阵分解（MF）的技术已被证明是有效的。然而，现有的MF方法存在两个主要问题：（1）由于集中式模型训练机制导致的昂贵计算和存储：集中式学习者必须维护整个用户-项目评分矩阵，以及潜在的巨大低秩矩阵。（2）隐私问题：用户的偏好可能通过集中式学习者泄露给恶意攻击者。为了解决这些问题，我们提出了一个用于POI推荐的去中心化MF（DMF）框架。具体来说，而不是维护所有低秩矩阵和敏感评分数据进行训练，我们提出了一种基于随机游走的去中心化训练技术，在每个用户的端点（例如手机和平板）上训练MF模型。通过这样做，每个用户的评分仍然掌握在自己手中，并且此外，去中心化学习可以视为具有多个学习者（用户）的分布式学习，从而缓解计算和存储问题。在两个真实数据集上的实验结果表明，与经典和最先进的潜在因子模型相比，DMF在精度和召回率方面显著提高了推荐性能。 显示英文摘要

摘要： Points of interest (POI) recommendation has been drawn much attention recently due to the increasing popularity of location-based networks, e.g., Foursquare and Yelp. Among the existing approaches to POI recommendation, Matrix Factorization (MF) based techniques have proven to be effective. However, existing MF approaches suffer from two major problems: (1) Expensive computations and storages due to the centralized model training mechanism: the centralized learners have to maintain the whole user-item rating matrix, and potentially huge low rank matrices. (2) Privacy issues: the users' preferences are at risk of leaking to malicious attackers via the centralized learner. To solve these, we present a Decentralized MF (DMF) framework for POI recommendation. Specifically, instead of maintaining all the low rank matrices and sensitive rating data for training, we propose a random walk based decentralized training technique to train MF models on each user's end, e.g., cell phone and Pad. By doing so, the ratings of each user are still kept on one's own hand, and moreover, decentralized learning can be taken as distributed learning with multi-learners (users), and thus alleviates the computation and storage issue. Experimental results on two real-world datasets demonstrate that, comparing with the classic and state-of-the-art latent factor models, DMF significantly improvements the recommendation performance in terms of precision and recall.