Hybrid MPI/OpenMP parallel asynchronous distributed alternating direction method of multipliers

Wang, Dongxia; Lei, Yongmei; Zhou, Jianhui

doi:10.1007/s00607-021-00968-0

Cited by 4 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, multi-block ADMM does not retain a convergence guarantee in general [12]. Various algorithms have been developed for optimization separability and provable convergence under coupled variables [15,30,54]. For instance, Liu et al [30] proposed a variant of ADMM for non-convex problems, which completely decouples variables by introducing linear approximation when updating all the coupled ones, thereby enabling parallel gradient descent.…”

Section: Convergence Analysismentioning

confidence: 99%

Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems

Togashi,

Abe,

Saito

2024

Proceedings of the ACM Web Conference 2024

View full text Add to dashboard Cite

Typical recommendation and ranking methods aim to optimize the satisfaction of users, but they are often oblivious to their impact on the items (e.g., products, jobs, news, video) and their providers. However, there has been a growing understanding that the latter is crucial to consider for a wide range of applications, since it determines the utility of those being recommended. Prior approaches to fairness-aware recommendation optimize a regularized objective to balance user satisfaction and item fairness based on some notion such as exposure fairness. These existing methods have been shown to be effective in controlling fairness, however, most of them are computationally inefficient, limiting their applications to only unrealistically small-scale situations. This indeed implies that the literature does not yet provide a solution to enable a flexible control of exposure in the industry-scale recommender systems where millions of users and items exist. To enable a computationally efficient exposure control even for such large-scale systems, this work develops a scalable, fast, and fair method called exposure-aware ADMM (exADMM). exADMM is based on implicit alternating least squares (iALS), a conventional scalable algorithm for collaborative filtering, but optimizes a regularized objective to achieve a flexible control of accuracy-fairness tradeoff. A particular technical challenge in developing exADMM is the fact that the fairness regularizer destroys the separability of optimization subproblems for users and items, which is an essential property to ensure the scalability of iALS. Therefore, we develop a set of optimization tools to enable yet scalable fairness control with provable convergence guarantees as a basis of our algorithm. Extensive experiments performed on three recommendation datasets demonstrate that exADMM enables a far more flexible fairness control than the vanilla version of iALS, while being much more computationally efficient than existing fairness-aware recommendation methods. CCS CONCEPTS• Information systems → Collaborative filtering.

show abstract