PDSR: A Privacy-Preserving Diversified Service Recommendation Method on Distributed Data
Lina Wang, Huan Yang, Yiran Shen, Chao Liu, Lianyong Qi, Xiuzhen, Cheng, Feng Li

TL;DR
This paper introduces PDSR, a privacy-preserving method for diversified service recommendation across distributed data sources, using LSH for privacy and a novel metric for balancing accuracy and diversity.
Contribution
It proposes a new privacy-preserving approach leveraging LSH and a novel accuracy-diversity metric with a 2-approximation algorithm for service recommendation.
Findings
Effective privacy-preserving data sharing across platforms
Improved diversified recommendation accuracy
Validated through extensive real dataset experiments
Abstract
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…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Recommender Systems and Techniques · Data Quality and Management
MethodsSoftmax · travel james · Attention Is All You Need · Focus
