使用关联数据云和FOAF词汇表改进基于内容的推荐系统

Hanane Zitouni, S. Meshoul, Kamel Taouche
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引用次数: 5

摘要

随着网络上发布的大量数据,用户根据自己的喜好获取相关信息变得更加困难。为了准确预测用户对商品的偏好,推荐系统应该使用有效的信息过滤引擎。此任务可以使用基于内容的过滤(CBF)或协作过滤或混合方法来实现。这项工作描述了一种CBF方法,旨在处理非结构化数据和新用户的问题,现有方法在这些问题上表现不佳。该方法的基本特征是使用语义空间向量模型将关联数据云纳入信息过滤过程。FOAF词汇表用于根据用户的FOAF配置文件定义用户之间的新距离度量。通过从关联数据云中提取额外的属性来增强非结构化项目的表示,从而减轻了分析这些项目内容的负担,从而降低了计算成本。我们报告了在MovieLens数据集上对所提出的方法进行的一些有希望的实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving content based recommender systems using linked data cloud and FOAF vocabulary
With the deluge of data published on the web, it becomes even more difficult for a user to get access to the relevant information based on his preferences. In order to accurately predict the preference a user would give to an item, recommender systems should use an effective information filtering engine. This task can be achieved using content based filtering (CBF) or collaborative filtering or a hybrid approach. This work describes an approach to CBF that aims to deal with the issues of unstructured data and new user on which existing approaches perform poorly. The basic feature of the proposed approach is to incorporate linked data cloud into the information filtering process using a semantic space vector model. FOAF vocabulary is used to define a new distance measure between users based on their FOAF profiles. Unstructured items representations are enhanced by additional attributes extracted from Linked data cloud which alleviates the burden to analyze the content of these items and therefore reduces the computational cost. We report on some promising experiments of the proposed approach performed on MovieLens data sets.
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