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使用基于本体的数据汇总开发语义感知推荐系统
Using Ontology-based Data Summarization to Develop Semantics-aware Recommender Systems |
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| 课程网址: |
http://videolectures.net/eswc2018_palmonari_recommender_systems/
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| 主讲教师: |
Matteo Palmonari
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| 开课单位: |
米兰大学
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| 开课时间: |
2018-07-10
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| 课程语种: |
英语
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| 中文简介: |
在当前以信息为中心的时代，推荐系统作为能够帮助用户完成日常决策任务的工具，正在获得发展势头。他们可以利用用户过去的行为，结合侧面/上下文信息，向他们推荐他们可能感兴趣的新项目或知识。在推荐过程中，链接数据（LD）已经被认为是一种有价值的信息来源，不仅在准确性方面，而且在结果的多样性和新颖性方面，它可以增强推荐系统的预测能力。在这个方向上，使用LD为推荐引擎提供支持的主要开放问题之一与特征选择有关：如何仅选择原始LD数据集的最相关子集，从而避免无用的数据处理和所谓的“维度过程”问题。在本文中，我们展示了基于本体的（链接的）数据摘要如何驱动对推荐系统有用的属性/特征的选择。特别是，我们将基于本体的数据摘要的全自动特征选择方法与更经典的方法进行了比较，并根据利用前k个所选特征的推荐系统的准确性和聚合多样性来评估这些方法的性能。我们基于与不同知识领域相关的数据集建立了一个实验测试平台。结果表明，基于本体的数据摘要驱动的特征选择过程对于支持LD的推荐系统是可行的。
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| 课程简介: |
In the current information-centric era, recommender systems are gaining momentum as tools able to assist users in daily decision-making tasks. They may exploit users’ past behavior combined with side/contextual information to suggest them new items or pieces of knowledge they might be interested in. Within the recommendation process, Linked Data (LD) have been already proposed as a valuable source of information to enhance the predictive power of recommender systems not only in terms of accuracy but also of diversity and novelty of results. In this direction, one of the main open issues in using LD to feed a recommendation engine is related to feature selection: how to select only the most relevant subset of the original LD dataset thus avoiding both useless processing of data and the so called “course of dimensionality” problem. In this paper we show how ontology-based (linked) data summarization can drive the selection of properties/features useful to a recommender system. In particular, we compare a fully automated feature selection method based on ontology-based data summaries with more classical ones and we evaluate the performance of these methods in terms of accuracy and aggregate diversity of a recommender system exploiting the top-k selected features. We set up an experimental testbed relying on datasets related to different knowledge domains. Results show the feasibility of a feature selection process driven by ontology-based data summaries for LD-enabled recommender systems.
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| 关 键 词: |
决策任务; 预测能力; 数据摘要
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| 课程来源: |
视频讲座网
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| 数据采集: |
2022-12-14：chenjy
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| 最后编审: |
2023-05-11：chenjy
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| 阅读次数: |
24
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图 书 馆