Clustering for closely similar recipes to extract spam recipes in user-generated recipe sites
Clustering for closely similar recipes to extract spam recipes in user-generated recipe sites
复制标题
对非常相似的食谱进行聚类，以提取用户生成的食谱网站中的垃圾邮件食谱
DOI：
10.1145/2837185.2837269
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Akiyo Nadamoto
中科院分区：
文献类型：
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作者：
Shunsuke Hanai;Hidetsugu Nanba;Akiyo Nadamoto
如今，许多用户生成的食谱网站都可以在互联网上访问。然而，在用户生成的食谱网站上，有各种垃圾食谱页面，这些页面描述了非常相似的食谱，需要特殊的烹饪设备，没有准备解释。这些垃圾邮件食谱对用户没有用处。事实上，它们阻碍了用户对食谱的搜索。在本文中，我们将相似的食谱作为提取垃圾食谱的第一步。如果用户搜索结果可以分类，以识别非常相似的食谱，用户的食谱搜索将更容易和更有效率。人们提出了许多类型的聚类工具，但仅使用现有的聚类工具很难对相似的食谱进行聚类，因为食谱网站具有独特的页面结构，包括标题、配料、说明(准备说明)和评论。每个部分的单词的重要性各不相同。我们提出了一种基于页面结构和重要词的用户生成菜谱网站的聚类方法。接下来，我们进行了一项实验，以衡量我们提出的方法的好处。实验结果表明，本文提出的方法对相似食谱进行分类是有效的。
Nowadays, many user-generated recipe sites are accessible on the internet. On user-generated recipe sites, however, are various spam recipe pages that describe closely similar recipes requiring special cooking equipment, with no preparation explanations. These spam recipes are not useful for users. In fact, they impede user's recipe searches. In this paper, we target closely similar recipes as a first step in extracting spam recipes. If user search results could be classified to identify closely similar recipes, user's recipe searches would be easier and more productive. Clustering tools of many kinds are proposed, but it is difficult to cluster closely similar recipes using only existing clustering tools because recipe sites have a unique page structure comprising a title, ingredients, directions (preparation instructions), and comments. The importance of words from each part differs. We propose a clustering method for user-generated recipe sites based on page structure and important words. Next, we conducted an experiment to measure the benefits of our proposed method. The result of experiment presents the benefits of our proposed method which classify the closely similar recipes.