论文标题
用于协作过滤模糊的过程模型模型
Blurring-Sharpening Process Models for Collaborative Filtering
论文作者
论文摘要
协作过滤是推荐系统最基本的主题之一。已经提出了各种方法来进行协作过滤，从矩阵分解到图形卷积方法。受到基于图滤波的方法和基于得分的生成模型（SGM）的最新成功的启发，我们提出了一种新颖的模糊过程模型（BSPM）的概念。 SGMS和BSPM具有相同的处理理念，即可以发现新信息（例如，在SGMS的情况下生成新图像），而原始信息首先受到扰动，然后恢复到其原始形式。但是，SGM和我们的BSPM处理不同类型的信息，其最佳扰动和恢复过程具有根本的差异。因此，我们的BSPM与SGM具有不同的形式。此外，我们的概念不仅归功于许多现有的协作过滤模型，而且在三个基准数据集（Gowalla，Yelp2018和Amazon-Book）中，在召回和NDCG方面的表现都优于召回和NDCG。此外，我们方法的处理时间与其他快速基线相当。我们提出的概念在将来具有很大的潜力，可以通过设计更好的模糊（即扰动）和锐化（即恢复）过程来增强，而不是我们在本文中使用的过程。
Collaborative filtering is one of the most fundamental topics for recommender systems. Various methods have been proposed for collaborative filtering, ranging from matrix factorization to graph convolutional methods. Being inspired by recent successes of graph filtering-based methods and score-based generative models (SGMs), we present a novel concept of blurring-sharpening process model (BSPM). SGMs and BSPMs share the same processing philosophy that new information can be discovered (e.g., new images are generated in the case of SGMs) while original information is first perturbed and then recovered to its original form. However, SGMs and our BSPMs deal with different types of information, and their optimal perturbation and recovery processes have fundamental discrepancies. Therefore, our BSPMs have different forms from SGMs. In addition, our concept not only theoretically subsumes many existing collaborative filtering models but also outperforms them in terms of Recall and NDCG in the three benchmark datasets, Gowalla, Yelp2018, and Amazon-book. In addition, the processing time of our method is comparable to other fast baselines. Our proposed concept has much potential in the future to be enhanced by designing better blurring (i.e., perturbation) and sharpening (i.e., recovery) processes than what we use in this paper.
