Optimizing recommender system using federated clustering

Ilya Tsiamchyk; Guanghui Wang; Fang Zuo; Xiaolin Huang

doi:10.1117/12.2691861

23 August 2023 Optimizing recommender system using federated clustering

Ilya Tsiamchyk, Guanghui Wang, Fang Zuo, Xiaolin Huang

Proceedings Volume 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023); 127843F (2023) https://doi.org/10.1117/12.2691861
Event: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), 2023, Kaifeng, China

Abstract

Currently, recommender systems are widely applied in various fields. However, due to the limited needs and special circumstances of users to be recommended, it is difficult for a recommender system to cover all users' interest lists at the same time. In this work, we present a kind of optimized federated clustering scheme (OP-Fed-Clustering) for users' private tendency data. The scheme starts by coding the initial data objects to protect privacy and then optimizes the assignment of data points based on object similarity. We also validates the algorithm's effectiveness on the FoodRecipe dataset and compares the algorithm to initial K-FED. Our tentative data show that the effectiveness of the proposed OP-Fed-Clustering algorithm, demonstrating universally superior performance while preserving user data confidentiality.

Citation Download Citation

Ilya Tsiamchyk, Guanghui Wang, Fang Zuo, and Xiaolin Huang "Optimizing recommender system using federated clustering", Proc. SPIE 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 127843F (23 August 2023); https://doi.org/10.1117/12.2691861

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available