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Ranking Database Queries with User Feedback: A Neural Network Approach

机译：使用用户反馈对数据库查询进行排名：一种神经网络方法

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摘要

Currently, websites on the Internet serving structured data allow users to perform search based on simple equality or range constraints on data attributes. However, to begin with, users may not know what is desirable to them precisely, to be able to express it accurately in terms of primitive equality or range constraints. Additionally, in most websites, the results provided to users can be sorted with respect to values of any one particular attribute at a time. For the user, this is like searching for a needle in a haystack because the user's notion of interesting objects is generally a function of multiple attributes. In this paper, we develop an approach to (ⅰ) support a family of functions involving multiple attributes to rank the tuples, and (ⅱ) improve the ranking of results returned to the user by incorporating user feedback (to learn user's notion of interestingness) with the help of a neural network. The user feedback driven approach is effective in modeling a user's intuitive sense of desirability of a tuple, a notion that is otherwise near impossible to quantify mathematically. To prove the effectiveness of our approach, we have built a middleware for an application domain that implements and evaluates these ideas.
机译：当前，互联网上提供结构化数据的网站允许用户基于对数据属性的简单相等性或范围约束来执行搜索。但是，一开始，用户可能不知道精确地期望他们什么，以便能够根据原始相等性或范围约束准确地表达它。另外，在大多数网站中，可以一次针对任何一个特定属性的值对提供给用户的结果进行排序。对于用户而言，这就像在大海捞针中寻找针头一样，因为用户对有趣对象的观念通常是多种属性的函数。在本文中，我们开发了一种方法（ⅰ）支持涉及多个属性的功能家族以对元组进行排名，并且（ⅱ）通过合并用户反馈来提高返回给用户的结果的排名（以了解用户的兴趣度）在神经网络的帮助下。用户反馈驱动的方法有效地建模了用户对元组的期望的直观感觉，否则该概念几乎不可能以数学方式量化。为了证明我们的方法的有效性，我们为实现和评估这些想法的应用程序域构建了中间件。

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