원문정보
초록
영어
Privacy is an important issue in Context-aware recommender systems (CARSs). In this paper, we propose a privacy-preserving CARS in which a user can limit the contextual information submitted to the server without sacrificing a significant recommendation accuracy. Specifically, for users, we introduce a client-side algorithm that the user can employ to generalize its context to some extent, in order to protect her privacy. For the recommendation server, two server-side recommendation algorithms are proposed, which operate under the condition that only a generalized user context is given. The experimental results have shown that, using our approaches, the user context privacy can be achieved without a significant degradation of the recommendation accuracy.
목차
1. Introduction
2. Background and Related Work
2.1. Context-aware Recommender System
2.2 Privacy in Context-aware System
3. Problem Formulation
3.1 System Model
3.2. Design Goals
3.3 Threat Model
4. Solution
4.1 Client-side Context Generalization
4.2. Server-side Recommendation
5. Experiments and Analysis
5.1. Dataset
5.2. Metrics
5.3 Results
6. Conclusion
Acknowledgments
References