Streaming Algorithms for News and Scientific Literature Recommendation: Submodular Maximization with a d-Knapsack Constraint

TL;DR

This paper introduces a streaming algorithm for maximizing monotone submodular functions under a d-knapsack constraint, enabling efficient, single-pass processing with significant speed and memory improvements for news and scientific literature recommendation.

Contribution

It presents a novel streaming algorithm with provable approximation guarantees for submodular maximization under d-knapsack constraints, applicable to real-world recommendation systems.

Findings

Achieves a /(1+2d)-approximation of the optimal value.

Provides significant speedup and memory savings in experiments.

Effective for news and scientific literature recommendation tasks.

Abstract

Submodular maximization problems belong to the family of combinatorial optimization problems and enjoy wide applications. In this paper, we focus on the problem of maximizing a monotone submodular function subject to a $d$-knapsack constraint, for which we propose a streaming algorithm that achieves a $\left(\frac{1}{1+2d}-\epsilon\right)$-approximation of the optimal value, while it only needs one single pass through the dataset without storing all the data in the memory. In our experiments, we extensively evaluate the effectiveness of our proposed algorithm via two applications: news recommendation and scientific literature recommendation. It is observed that the proposed streaming algorithm achieves both execution speedup and memory saving by several orders of magnitude, compared with existing approaches.