Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity

TL;DR

This paper introduces a fast, scalable greedy algorithm for MAP inference in DPPs, enhancing recommendation diversity and relevance in real-time applications.

Contribution

A novel accelerated greedy algorithm for DPP MAP inference that adapts to local repulsion scenarios and improves computational efficiency.

Findings

Significantly faster than existing methods

Achieves better relevance-diversity trade-off

Confirmed effectiveness in online A/B tests

Abstract

The determinantal point process (DPP) is an elegant probabilistic model of repulsion with applications in various machine learning tasks including summarization and search. However, the maximum a posteriori (MAP) inference for DPP which plays an important role in many applications is NP-hard, and even the popular greedy algorithm can still be too computationally expensive to be used in large-scale real-time scenarios. To overcome the computational challenge, in this paper, we propose a novel algorithm to greatly accelerate the greedy MAP inference for DPP. In addition, our algorithm also adapts to scenarios where the repulsion is only required among nearby few items in the result sequence. We apply the proposed algorithm to generate relevant and diverse recommendations. Experimental results show that our proposed algorithm is significantly faster than state-of-the-art competitors, and provides a better relevance-diversity trade-off on several public datasets, which is also confirmed in an online A/B test.