ArgueNet: an argument-based recommender system for solving Web search queries
ArgueNet: an argument-based recommender system for solving Web search queries
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ArgueNet：用于解决网络搜索查询的基于参数的推荐系统
DOI：
10.1109/is.2004.1344683
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Ana Gabriela Maguitman
中科院分区：
文献类型：
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作者：
C. Chesñevar;Ana Gabriela Maguitman
在过去的几年中，已经开发了几种专门用于改进Web搜索的技术。大多数现有的方法仍然是有限的，主要是由于缺乏对结果排序的定性标准，以及对指导搜索的用户偏好不敏感。与此同时，可否定论证作为一种成功的人工智能方法，在智能体理论、知识工程和法律推理等许多领域应用，为常识性定性推理建模。本文介绍了ArgueNet，一个根据用户声明性指定的偏好标准对搜索结果进行分类的推荐系统。提出的方法将传统的Web搜索引擎与一个可行的论证框架相结合。
In the last years several specialized techniques for improving Web search have been developed. Most existing approaches are still limited, mainly due to the absence of qualitative criteria for ranking results and insensitivity to user preferences for guiding the search. At the same time, defeasible argumentation evolved as a successful approach in AI to model commonsense qualitative reasoning with applications in many areas, such as agent theory, knowledge engineering and legal reasoning. This paper presents ArgueNet, a recommender system that classifies search results according to preference criteria declaratively specified by the user. The proposed approach integrates a traditional Web search engine with a defeasible argumentation framework.