Query suggestion with diversification and personalization

被引：9
|
作者：
Jiang, Di [1 ]
Leung, Kenneth Wai-Ting [2 ]
Yang, Lingxiao [3 ]
Ng, Wilfred [2 ]
机构：
[1] Baidu Inc, Beijing, Peoples R China
[2] Hong Kong Univ Sci & Technol, Hong Kong, Hong Kong, Peoples R China
[3] Univ London London Sch Econ & Polit Sci, London WC2A 2AE, England
来源：
KNOWLEDGE-BASED SYSTEMS | 2015年 / 89卷
关键词：
Personalization; Diversification; Search engine;
D O I：
10.1016/j.knosys.2015.09.003
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Web search query suggestion is an important functionality that facilitates information seeking of search engine users. In existing work, the concepts of diversification and personalization have been individually introduced to query suggestion systems. In this paper, we propose a new query suggestion paradigm, Query Suggestion With Diversification and Personalization (QS-DP) to effectively integrate diversification and personalization into one unified framework. In the QS-DP, the suggested queries are effectively diversified to cover different facets of the input query while the ranking of the suggested queries are personalized to ensure that the top ones are those that align with a user's personal preferences. We evaluate QS-DP on a commercial search engine query log against several existing query suggestion methods. The experimental results verify our hypothesis that diversification and personalization can be effectively integrated and they are able to enhance each other within the QS-DP framework, which significantly outperforms several strong baselines with respect to a series of metrics. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：553 / 568
页数：16
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