2 research outputs found
Streaming Algorithms for Diversity Maximization with Fairness Constraints
Diversity maximization is a fundamental problem with wide applications in
data summarization, web search, and recommender systems. Given a set of
elements, it asks to select a subset of elements with maximum
\emph{diversity}, as quantified by the dissimilarities among the elements in
. In this paper, we focus on the diversity maximization problem with
fairness constraints in the streaming setting. Specifically, we consider the
max-min diversity objective, which selects a subset that maximizes the
minimum distance (dissimilarity) between any pair of distinct elements within
it. Assuming that the set is partitioned into disjoint groups by some
sensitive attribute, e.g., sex or race, ensuring \emph{fairness} requires that
the selected subset contains elements from each group .
A streaming algorithm should process sequentially in one pass and return a
subset with maximum \emph{diversity} while guaranteeing the fairness
constraint. Although diversity maximization has been extensively studied, the
only known algorithms that can work with the max-min diversity objective and
fairness constraints are very inefficient for data streams. Since diversity
maximization is NP-hard in general, we propose two approximation algorithms for
fair diversity maximization in data streams, the first of which is
-approximate and specific for , where
, and the second of which achieves a
-approximation for an arbitrary . Experimental
results on real-world and synthetic datasets show that both algorithms provide
solutions of comparable quality to the state-of-the-art algorithms while
running several orders of magnitude faster in the streaming setting.Comment: 13 pages, 11 figures; published in ICDE 202
Maximizing diversity over clustered data*
| openaire: EC/H2020/871042/EU//SoBigData-PlusPlusMaximum diversity aims at selecting a diverse set of high-quality objects from a collection, which is a fundamental problem and has a wide range of applications, e.g., in Web search. Diversity under a uniform or partition matroid constraint naturally describes useful cardinality or budget requirements, and admits simple approximation algorithms [5]. When applied to clustered data, however, popular algorithms such as picking objects iteratively and performing local search lose their approximation guarantees towards maximum intra-cluster diversity because they fail to optimize the objective in a global manner. We propose an algorithm that greedily adds a pair of objects instead of a singleton, and which attains a constant approximation factor over clustered data. We further extend the algorithm to the case of monotone and submodular quality function, and under a partition matroid constraint. We also devise a technique to make our algorithm scalable, and on the way we obtain a modification that gives better solutions in practice while maintaining the approximation guarantee in theory. Our algorithm achieves excellent performance, compared to strong baselines in a mix of synthetic and real-world datasets.Peer reviewe