Naturally Private Recommendations with Determinantal Point Processes (2405.13677v1)
Abstract: Often we consider machine learning models or statistical analysis methods which we endeavour to alter, by introducing a randomized mechanism, to make the model conform to a differential privacy constraint. However, certain models can often be implicitly differentially private or require significantly fewer alterations. In this work, we discuss Determinantal Point Processes (DPPs) which are dispersion models that balance recommendations based on both the popularity and the diversity of the content. We introduce DPPs, derive and discuss the alternations required for them to satisfy epsilon-Differential Privacy and provide an analysis of their sensitivity. We conclude by proposing simple alternatives to DPPs which would make them more efficient with respect to their privacy-utility trade-off.
- Odile Macchi. The coincidence approach to stochastic point processes. Advances in Applied Probability, 7(1):83–122, 1975.
- Statistical properties of determinantal point processes in high-dimensional euclidean spaces. Physical Review E, 79(4):041108, 2009.
- Determinantal point processes for machine learning. Foundations and Trends® in Machine Learning, 5(2–3):123–286, 2012.
- Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration. Advances in neural information processing systems, 31, 2018.
- Exact sampling from determinantal point processes. arXiv preprint arXiv:1609.06840, 2016.
- Practical diversified recommendations on youtube with determinantal point processes. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pages 2165–2173, 2018.
- Fast greedy map inference for determinantal point process to improve recommendation diversity. Advances in Neural Information Processing Systems, 31, 2018.
- Enhancing recommendation diversity using determinantal point processes on knowledge graphs. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2001–2004, 2020.
- Diversified interactive recommendation with implicit feedback. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 4932–4939, 2020.
- Recent advances in diversified recommendation. arXiv preprint arXiv:1905.06589, 2019.
- Monte carlo with determinantal point processes. 2020.
- Regression and classification using gaussian process priors. Bayesian statistics, 6:475, 1998.
- The rotation of eigenvectors by a perturbation. iii. SIAM Journal on Numerical Analysis, 7(1):1–46, 1970.
- Mikio Ludwig Braun. Spectral properties of the kernel matrix and their relation to kernel methods in machine learning. PhD thesis, Universitäts-und Landesbibliothek Bonn, 2005.
- A multiplicative weights mechanism for privacy-preserving data analysis. In 2010 IEEE 51st annual symposium on foundations of computer science, pages 61–70. IEEE, 2010.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Collections
Sign up for free to add this paper to one or more collections.