A Linked Data Recommender System Using a Neighborhood-Based Graph Kernel
A Linked Data Recommender System Using a Neighborhood-Based Graph Kernel
复制标题
DOI：
10.1007/978-3-319-10491-1_10
复制
发表时间：
2014-09
期刊：
影响因子：
--
通讯作者：
V. Ostuni;T. D. Noia;R. Mirizzi;E. Sciascio
中科院分区：
文献类型：
--
作者：
V. Ostuni;T. D. Noia;R. Mirizzi;E. Sciascio
推荐系统（RS）的最终使命是帮助用户发现他们可能感兴趣的项目。基于内容的RS（Content-based RS）不仅需要获取尽可能多的信息，而且需要有效地处理这些信息，因此，具有大量语义相关数据的关联开放数据集（Linked Open Data，LOD）的蓬勃发展为CB-RS的发展提供了一个很好的机会。在本文中，我们提出了一个CB-RS，利用LOD和利润从一个基于邻域的图形内核。所提出的核能够通过匹配它们的局部邻域图来计算语义项相似度。在MovieLens数据集上的实验结果表明，该方法在准确性和新奇性方面优于其他竞争方法。
The ultimate mission of a Recommender System (RS) is to help users discover items they might be interested in. In order to be really useful for the end-user, Content-based (CB) RSs need both to harvest as much information as possible about such items and to effectively handle it. The boom of Linked Open Data (LOD) datasets with their huge amount of semantically interrelated data is thus a great opportunity for boosting CB-RSs. In this paper we present a CB-RS that leverages LOD and profits from aneighborhood-based graph kernel. The proposed kernel is able to compute semantic item similarities by matching their local neighborhood graphs. Experimental evaluation on the MovieLens dataset shows that the proposed approach outperforms in terms of accuracy and novelty other competitive approaches.