Multi-Selection for Recommendation Systems
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 $\epsilon$ 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.
Code (0)
등록된 구현이 없습니다.
Tasks
Movie RecommendationRecommendation SystemsSimilar Papers 제목 키워드 기반
Improving Recommendation System Serendipity Through Lexicase Selection
Recommender systems influence almost every aspect of our digital lives. Unfortunately, in striving to give us what we want, they end up restricting our open-mindedness. Current recommender systems promote echo chambers, …
DiversityRecommendation SystemsRethinking Recommender Systems: Cluster-based Algorithm Selection
Cluster-based algorithm selection deals with selecting recommendation algorithms on clusters of users to obtain performance gains. No studies have been attempted for many combinations of clustering approaches and recomme…
ClusteringRecommendation SystemsEnsemble Boost: Greedy Selection for Superior Recommender Systems
Ensemble techniques have demonstrated remarkable success in improving predictive performance across various domains by aggregating predictions from multiple models [1]. In the realm of recommender systems, this research …
Recommendation SystemsCRUISE on Quantum Computing for Feature Selection in Recommender Systems
Using Quantum Computers to solve problems in Recommender Systems that classical computers cannot address is a worthwhile research topic. In this paper, we use Quantum Annealers to address the feature selection problem in…
counterfactualfeature selectionRecommendation SystemsMetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection
Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every user. We observe this ubiquitous phenomeno…
Meta-LearningModel SelectionRecommendation Systems