I am complex: cluster me, don't just rank me

BEWEB '11 Pub Date : 2011-03-25 DOI:10.1145/1966883.1966885
S. Amer-Yahia
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引用次数: 2

Abstract

A large number of online applications are built over high dimensional data. That is the case for shopping where products have several features (e.g., price and color), dating where personal profiles are described using several dimensions (e.g., physical features and political views), and entertainment (e.g., movie genre and director, restaurant ambiance and location). In addition, in some applications, items may be accompanied with qualitative data such as movie and restaurant reviews. The typical way users find items in those applications is by entering a keyword query and receiving a ranked list of relevant results. Ideally, just like in Web search, users would want to spend little time before finding a satisfactory item. In practice, due the query output size, the high dimensionality of items, and in some cases, the presence of qualitative data, users tend to spend a lot of time trying to understand correlations between item features and item quality. In this talk, I will argue that the 10-blue links experience we are used to in Web search, keywords as input - ranked list as output, is inappropriate when querying and ranking high dimensional data. I will describe two applications: exploring qualitative data and ranked querying of structured data.
我很复杂:群集我,不要只是给我排序
大量的在线应用程序是建立在高维数据之上的。在购物时,产品有多种特征(例如,价格和颜色);在约会时,个人资料用多种维度来描述(例如,身体特征和政治观点);在娱乐时,电影类型和导演、餐厅氛围和地点都是如此。此外,在一些应用程序中,项目可能伴随着定性数据,如电影和餐馆评论。用户在这些应用程序中查找项目的典型方式是输入关键字查询,然后收到相关结果的排序列表。理想情况下,就像在Web搜索中一样,用户希望在找到满意的项目之前花费很少的时间。在实践中,由于查询输出的大小、项目的高维度,以及在某些情况下,定性数据的存在,用户倾向于花费大量时间试图理解项目特征和项目质量之间的相关性。在这次演讲中,我将论证我们在Web搜索中习惯的10蓝链接体验,关键词作为输入-排名列表作为输出,在查询和排名高维数据时是不合适的。我将描述两个应用:探索定性数据和结构化数据的排序查询。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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