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Introducing linked open data in graph-based recommender systems

机译：在基于图的推荐系统中引入链接的开放数据

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摘要

Thanks to the recent spread of the Linked Open Data (LOD) initiative, a huge amount of machine-readable knowledge encoded as RDF statements is today available in the so-called LOD cloud. Accordingly, a big effort is now spent to investigate to what extent such information can be exploited to develop new knowledge-based services or to improve the effectiveness of knowledge-intensive platforms as Recommender Systems (RS). To this end, in this article we study the impact of the exogenous knowledge coming from the LOD cloud on the overall performance of a graph-based recommendation framework. Specifically, we propose a methodology to automatically feed a graph-based RS with features gathered from the LOD cloud and we analyze the impact of several widespread feature selection techniques in such recommendation settings. The experimental evaluation, performed on three state-of-the-art datasets, provided several outcomes: first, information extracted from the LOD cloud can significantly improve the performance of a graph-based RS. Next, experiments showed a clear correlation between the choice of the feature selection technique and the ability of the algorithm to maximize specific evaluation metrics, as accuracy or diversity of the recommendations. Moreover, our graph-based algorithm fed with LOD-based features was able to overcome several baselines, as collaborative filtering and matrix factorization.
机译：由于链接开放数据（LOD）计划的最近传播，如今在所谓的LOD云中提供了大量编码为RDF语句的机器可读知识。因此，现在花费大量的精力来研究可以在多大程度上利用这些信息来开发新的基于知识的服务或提高知识密集型平台（如推荐系统）的有效性。为此，在本文中，我们研究了来自LOD云的外来知识对基于图的推荐框架的整体性能的影响。具体而言，我们提出了一种方法，该方法可自动将基于LOD云中收集的特征供入基于图的RS，并分析这种推荐设置中几种广泛使用的特征选择技术的影响。在三个最新数据集上进行的实验评估提供了以下结果：首先，从LOD云中提取的信息可以显着提高基于图形的RS的性能。接下来，实验表明，特征选择技术的选择与算法最大化特定评估指标的算法的能力之间存在明显的相关性，即建议的准确性或多样性。此外，我们的基于图的算法结合了基于LOD的功能，能够克服多个基线，例如协作过滤和矩阵分解。

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