Improving the Diversity of Top-N Recommendation via Determinantal Point Process
Improving the Diversity of Top-N Recommendation via Determinantal Point Process
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发表时间：
2017-09
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通讯作者：
Laming Chen;Guoxin Zhang;Hanning Zhou
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作者：
Laming Chen;Guoxin Zhang;Hanning Zhou
推荐系统主要负责帮助用户发现他们可能感兴趣的项目。许多推荐算法都是建立在相似性度量的基础上的，这通常会导致列表内的多样性较低。在捕捉用户兴趣的整个范围方面的不足往往导致满意度不高。为了解决这个问题，近年来，越来越多的注意力集中在提高推荐结果的多样性上。在本文中，我们提出了一种新的方法，以提高前$N$推荐结果的多样性的基础上的决定点过程（DPP），这是一个优雅的模型，用于表征排斥现象。我们提出了一种加速算法，大大加快了结果推理的过程，使我们的算法适用于大规模的场景。我们还将一个可调参数的DPP模型，使用户能够顺利地控制多样性的水平。引入更多的多样性度量来更好地评估多样化算法。我们已经评估了我们的算法在几个公共数据集上，并与其他参考算法进行了彻底的比较。结果表明，我们提出的算法提供了一个更好的准确性多样性的权衡与可比的效率。
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.