PathRank: a novel node ranking measure on a heterogeneous graph for recommender systems
PathRank: a novel node ranking measure on a heterogeneous graph for recommender systems
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DOI：
10.1145/2396761.2398488
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发表时间：
2012-10
期刊：
影响因子：
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通讯作者：
S. Lee;Sungchan Park;Minsuk Kahng;Sang-goo Lee
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文献类型：
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作者：
S. Lee;Sungchan Park;Minsuk Kahng;Sang-goo Lee
在本文中，我们提出了一种新的随机游走的节点排名措施，PathRank，这是定义在一个异构图上扩展的个性化PageRank算法。我们提出的措施不仅可以利用背后的语义不同类型的节点和边缘的异构图，但它也可以模拟各种推荐语义，如协同过滤，基于内容的过滤，以及它们的组合。实验结果表明，与现有的推荐方法相比，PathRank能产生更多样、更有效的推荐结果。
In this paper, we present a novel random-walk based node ranking measure, PathRank, which is defined on a heterogeneous graph by extending the Personalized PageRank algorithm. Not only can our proposed measure exploit the semantics behind the different types of nodes and edges in a heterogeneous graph, but also it can emulate various recommendation semantics such as collaborative filtering, content-based filtering, and their combinations. The experimental results show that PathRank can produce more various and effective recommendation results compared to existing approaches.