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PDSR: A Privacy-Preserving Diversified Service Recommendation Method on Distributed Data
IEEE Transactions on Services Computing ( IF 5.5 ) Pub Date : 2024-09-09 , DOI: 10.1109/tsc.2024.3455111
Lina Wang 1 , Huan Yang 2 , Yiran Shen 3 , Chao Liu 4 , Lianyong Qi 5 , Xiuzhen Cheng 1 , Feng Li 1
Affiliation  

The last decade has witnessed a tremendous growth of service computing, while efficient service recommendation methods are desired to recommend high-quality services to users. It is well known that collaborative filtering is one of the most popular methods for service recommendation based on QoS, and many existing proposals focus on improving recommendation accuracy, i.e., recommending high-quality redundant services. Nevertheless, users may have different requirements on QoS, and hence diversified recommendation has been attracting increasing attention in recent years to fulfill users’ diverse demands and to explore potential services. Unfortunately, the recommendation performances relies on a large volume of data (e.g., QoS data), whereas the data may be distributed across multiple platforms. Therefore, to enable data sharing across the different platforms for diversified service recommendation, we propose a Privacy-preserving Diversified Service Recommendation (PDSR) method. Specifically, we innovate in leveraging the Locality-Sensitive Hashing (LSH) mechanism such that privacy-preserved data sharing across different platforms is enabled to construct a service similarity graph. Based on the similarity graph, we propose a novel accuracy-diversity metric and design a 2-approximation algorithm to select $K$ services to recommend by maximizing the accuracy-diversity measure. Extensive experiments on real datasets are conducted to verify the efficacy of our PDSR method.

中文翻译:


PDSR:一种基于分布式数据的隐私保护多元化服务推荐方法



过去十年见证了服务计算的巨大增长,同时需要高效的服务推荐方法来向用户推荐高质量的服务。众所周知,协同过滤是基于 QoS 的服务推荐最流行的方法之一,现有的许多提案都侧重于提高推荐准确性,即推荐高质量的冗余服务。然而,用户对 QoS 的要求可能不同,因此近年来多样化推荐越来越受到关注,以满足用户多样化的需求并探索潜在的服务。遗憾的是,推荐性能依赖于大量数据(例如 QoS 数据),而数据可能分布在多个平台上。因此,为了实现不同平台之间的数据共享以实现多元化服务推荐,我们提出了一种隐私保护多元化服务推荐 (PDSR) 方法。具体来说,我们在利用位置敏感哈希 (LSH) 机制方面进行了创新,从而能够在不同平台之间实现隐私保护数据共享,从而构建服务相似性图。基于相似性图,我们提出了一种新的准确率-多样性指标,并设计了一种 2-近似算法,通过最大化准确率-多样性度量来选择 $K$ 个服务进行推荐。在真实数据集上进行了广泛的实验,以验证我们的 PDSR 方法的有效性。
更新日期:2024-09-09
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