Personalized Recommendations using Knowledge Graphs: A Probabilistic Logic Programming Approach
Personalized Recommendations using Knowledge Graphs: A Probabilistic Logic Programming Approach
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DOI：
10.1145/2959100.2959131
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发表时间：
2016-09
期刊：
影响因子：
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通讯作者：
R. Catherine;William W. Cohen
中科院分区：
文献类型：
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作者：
R. Catherine;William W. Cohen
利用知识图提高推荐系统的性能是一项重要的任务。过去已经提出了许多混合系统，它们使用基于内容和协同过滤技术的混合来提高性能。最近，一些工作集中在使用外部知识图（KG）来补充基于内容的推荐的推荐上。在本文中，我们研究了三种方法，使KG为基础的建议，使用一个通用的概率逻辑系统称为ProPPR。其中最简单的模型，multitySim，只使用图中的链接。然后，我们将模型扩展到TypeSim，它也使用实体的类型来增强其泛化能力。接下来，我们开发了一个基于图的潜在因子模型GraphLF，它结合了潜在因子分解与图的优势。我们将我们的方法与最近提出的两个大型数据集Yelp和MovieLens-100 K上的最先进的图形推荐方法进行了比较。实验表明，我们的方法可以给大的性能改善。此外，我们证明了知识图在数据集稀疏时具有最大优势，并且随着更多的训练数据变得可用而逐渐变得冗余，因此在冷启动设置中最有用。
Improving the performance of recommender systems using knowledge graphs is an important task. There have been many hybrid systems proposed in the past that use a mix of content-based and collaborative filtering techniques to boost the performance. More recently, some work has focused on recommendations that use external knowledge graphs (KGs) to supplement content-based recommendation. In this paper, we investigate three methods for making KG based recommendations using a general-purpose probabilistic logic system called ProPPR. The simplest of the models, EntitySim, uses only the links of the graph. We then extend the model to TypeSim that also uses the types of the entities to boost its generalization capabilities. Next, we develop a graph based latent factor model, GraphLF, which combines the strengths of latent factorization with graphs. We compare our approaches to a recently proposed state-of-the-art graph recommendation method on two large datasets, Yelp and MovieLens-100K. The experiments illustrate that our approaches can give large performance improvements. Additionally, we demonstrate that knowledge graphs give maximum advantage when the dataset is sparse, and gradually become redundant as more training data becomes available, and hence are most useful in cold-start settings.