Improving the Diversity of Top-N Recommendation via Determinantal Point Process

Recommender systems take the key responsibility to help users discover items that they might be interested in. Many recommendation algorithms are built upon similarity measures, which usually result in low intra-list diversity. The deficiency in capturing the whole range of user interest often leads to poor satisfaction. To solve this problem, increasing attention has been paid on improving the diversity of recommendation results in recent years. In this paper, we propose a novel method to improve the diversity of top-$N$ recommendation results based on the determinantal point process (DPP), which is an elegant model for characterizing the repulsion phenomenon. We propose an acceleration algorithm to greatly speed up the process of the result inference, making our algorithm practical for large-scale scenarios. We also incorporate a tunable parameter into the DPP model which allows the users to smoothly control the level of diversity. More diversity metrics are introduced to better evaluate diversification algorithms. We have evaluated our algorithm on several public datasets, and compared it thoroughly with other reference algorithms. Results show that our proposed algorithm provides a much better accuracy-diversity trade-off with comparable efficiency.