多选推荐提升隐私保护下的推荐效果，接近最优性能。
Multi-Selection for Recommendation Systems
- 服务器返回多个推荐项和本地模型，用户本地选择最匹配的
- 多选模式下推荐效用达97%，优于非多选的91%
- 在ε≈1的局部差分隐私下仍保持高推荐质量，适合隐私敏感场景
我们构建了一个多选推荐模型，用于在推荐系统中回答差分隐私查询。服务器向用户返回多个推荐项和一个“本地模型”，用户可在设备上本地运行该模型，选择最符合其私有特征的项目。实验基于在Movielens 25M数据集上训练的深度神经网络作为真实推荐基准。在多选范式下，平均推荐效用约为基准最优效用的97%，同时在ε≈1的局部差分隐私约束下，对邻近用户特征向量保持隐私保护。相较之下，非多选模式在相同条件下平均推荐效用为91%。
原文摘要 · Abstract (English)
We present the construction of a multi-selection model to answer differentially private queries in the context of recommendation systems. The server sends back multiple recommendations and a ``local model'' to the user, which the user can run locally on its device to select the item that best fits its private features. We study a setup where the server uses a deep neural network (trained on the Movielens 25M dataset as the ground truth for movie recommendation. In the multi-selection paradigm, the average recommendation utility is approximately 97\% of the optimal utility (as determined by the ground truth neural network) while maintaining a local differential privacy guarantee with $ε$ ranging around 1 with respect to feature vectors of neighboring users. This is in comparison to an average recommendation utility of 91\% in the non-multi-selection regime under the same constraints.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。