Streaming Algorithms for News and Scientific Literature Recommendation: Submodular Maximization with a d-Knapsack Constraint
This work addresses efficient recommendation systems for news and scientific literature by providing a scalable streaming solution, though it is incremental as it builds on known submodular optimization techniques.
The paper tackles the problem of maximizing a monotone submodular function under a d-knapsack constraint by proposing a streaming algorithm that achieves a (1/(1+2d)-ε)-approximation, requiring only one pass through the data without full storage. In experiments on news and scientific literature recommendation, the algorithm shows execution speedup and memory saving by several orders of magnitude compared to existing methods.
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 $\left(\frac{1}{1+2d}-ε\right)$-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.