图 书 馆
网络广告：利用理论与数据优化交易市场An Optimization Framework for Query Recommendation |
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| 课程网址: | http://videolectures.net/wsdm2010_castillo_apf/ |
| 主讲教师: | Carlos Castillo |
| 开课单位: | 加泰罗尼亚技术中心 |
| 开课时间: | 2010-10-12 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | 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 reformulation 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. |
| 关 键 词: | 计算机科学; 数据挖掘; 优化交易市场 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-11：liush |
| 阅读次数: | 33 |