An optimization framework for query recommendation
An optimization framework for query recommendation
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DOI：
10.1145/1718487.1718508
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发表时间：
2010-02
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影响因子：
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通讯作者：
A. Anagnostopoulos;L. Becchetti;C. Castillo;A. Gionis
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文献类型：
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作者：
A. Anagnostopoulos;L. Becchetti;C. Castillo;A. Gionis
查询建议是现代搜索引擎不可或缺的一部分。查询建议的目的是在搜索信息时促进用户。查询建议还允许用户探索与其信息需求相关的概念。在本文中，我们对查询建议问题进行了正式处理。在我们的框架中，我们通过概率的改革图或查询流图[Boldi等。 CIKM 2008]。用户提交的一系列查询序列可以看作是该图上的路径。将分数值分配给查询使我们能够定义合适的效用函数，并考虑通过查询流图上的重新印度路径实现的预期效用。提供建议可以看作是在查询流图中添加快捷方式，以“推动”用户的重新制定路径，以至于用户更有可能遵循具有较大预期效用的路径。我们详细讨论拟议框架中最重要的问题。特别是，我们提供了有意义的实用程序功能以优化的示例，我们讨论了如何估计建议对重新制定概率的影响，我们解决了我们考虑的优化问题的复杂性，我们建议有效的算法解决方案，并验证我们的模型和算法进行广泛的实验。我们的技术可以应用于可以将用户行为建模为Markov过程的其他情况。
Query recommendation is an integral part of modern search engines. The goal of query recommendation is to facilitate users while searching for information. Query recommendation also allows users to explore concepts related to their information needs. In this paper, we present a formal treatment of the problem of query recommendation. In our framework we model the querying behavior of users by a probabilistic reformula- tion graph, or query-flow graph [Boldi et al. CIKM 2008]. A sequence of queries submitted by a user can be seen as a path on this graph. Assigning score values to queries allows us to define suitable utility functions and to consider the expected utility achieved by a reformulation path on the query-flow graph. Providing recommendations can be seen as adding shortcuts in the query-flow graph that "nudge" the reformulation paths of users, in such a way that users are more likely to follow paths with larger expected utility. We discuss in detail the most important questions that arise in the proposed framework. In particular, we provide examples of meaningful utility functions to optimize, we discuss how to estimate the effect of recommendations on the reformulation probabilities, we address the complexity of the optimization problems that we consider, we suggest efficient algorithmic solutions, and we validate our models and algorithms with extensive experimentation. Our techniques can be applied to other scenarios where user behavior can be modeled as a Markov process.