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User Preference Web Search -- Experiments with a System Connecting Web and User

机译：用户首选项Web搜索-尝试使用连接Web和用户的系统

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摘要

We present models, methods, implementations and experiments with a system enabling personalized web search for many users with different preferences. The system consists of a web information extraction part, a text search engine, a middleware supporting top-k answers and a user interface for querying and evaluation of search results. We integrate several tools (implementing our models and methods) into one framework connecting user with the web. The model represents user preferences with fuzzy sets and fuzzy logic, here understood as a scoring describing user satisfaction. This model can be acquired with explicit or implicit methods. Model-theoretic semantics is based on fuzzy description logic f-EL. User preference learning is based on our model of fuzzy inductive logic programming. Our system works both for English and Slovak resources. The primary application domain are job offers and job search, however we show extension to mutual investment funds search and a possibility of extension into other application domains. Our top-k search is optimized with own heuristics and repository with special indexes. Our model was experimentally implemented, the integration was tested and is web accessible. We focus on experiments with several users and measure their satisfaction according to correlation coefficients.
机译：我们提供的系统模型，方法，实现和实验，可为许多具有不同偏好的用户提供个性化的网络搜索。该系统由Web信息提取部分，文本搜索引擎，支持top-k答案的中间件以及用于查询和评估搜索结果的用户界面组成。我们将几种工具（实现模型和方法）集成到一个将用户与网络连接的框架中。该模型用模糊集和模糊逻辑表示用户偏好，这里理解为描述用户满意度的得分。可以使用显式或隐式方法获取此模型。模型理论语义基于模糊描述逻辑f-EL。用户偏好学习基于我们的模糊归纳逻辑编程模型。我们的系统适用于英语和斯洛伐克语资源。主要的应用领域是工作机会和工作搜索，但是我们展示了扩展到共同投资基金搜索以及扩展到其他应用领域的可能性。我们的top-k搜索使用自己的试探法和带有特殊索引的存储库进行了优化。我们的模型是通过实验实现的，集成已经过测试并且可以通过网络访问。我们专注于与多个用户进行的实验，并根据相关系数衡量他们的满意度。

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