Automatic query recommendation using click-through data

被引：0

作者：

Dupret, Georges ^{[1
]}

Mendoza, Marcelo ^{[2
]}

机构：

[1] Yahoo Res Latin Amer, Blanco Encalada 2120, Santiago, Chile

[2] Univ Valparaiso, Dept Comp Sci, Valparaiso, Chile

来源：

PROFESSIONAL PRACTICE IN ARTIFICIAL INTELLIGENCE | 2006年 / 218卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a method to help a user redefine a query suggesting a list of similar queries. The method proposed is based on click-through data were sets of similar queries could be identified. Scientific literature shows that similar queries are useful for the identification of. different information needs behind a query. Unlike most previous work, in this paper we are focused on the discovery of better queries rather than related queries. We will show with experiments over real data that the identification of better queries is useful for query disambiguation and query specialization.

引用

页码：303 / +

页数：2

共 50 条

[31] Identifying and Classifying Named Entities from Click-through Data Effectively
Jiang, Xuan
Liu, Hongyan
Cheng, Hong
He, Jun
Du, Xiaoyong
[J]. ELECTRONIC-BUSINESS INTELLIGENCE: FOR CORPORATE COMPETITIVE ADVANTAGES IN THE AGE OF EMERGING TECHNOLOGIES & GLOBALIZATION, 2010, 14 : 476 - +
[32] Visually Interpreting Names as Demographic Attributes by Exploiting Click-Through Data
Chen, Yan-Ying
Kuo, Yin-Hsi
Wu, Chun-Che
Hsu, Winston H.
[J]. PROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2015, : 44 - 50
[33] Click fraud resistant methods for learning click-through rates
Immorlica, N
Jain, K
Mahdian, M
Talwar, K
[J]. INTERNET AND NETWORK ECONOMICS, PROCEEDINGS, 2005, 3828 : 34 - 45
[34] AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
Liu, Bin
Zhu, Chenxu
Li, Guilin
Zhang, Weinan
Lai, Jincai
Tang, Ruiming
He, Xiuqiang
Li, Zhenguo
Yu, Yong
[J]. KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, 2020, : 2636 - 2645
[35] Click Feedback-Aware Query Recommendation Using Adversarial Examples
Li, Ruirui
Li, Liangda
Wu, Xian
Zhou, Yunhong
Wang, Wei
[J]. WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), 2019, : 2978 - 2984
[36] Discovering user preferences by using time entries in click-through data to improve search engine results
Ramachandran, P
[J]. DISCOVERY SCIENCE, PROCEEDINGS, 2005, 3735 : 383 - 385
[37] Named Entity Mining from Click-Through Data Using Weakly Supervised Latent Dirichlet Allocation
Xu, Gu
Yang, Shuang-Hong
Li, Hang
[J]. KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2009, : 1365 - 1373
[38] Click-Through Rate Prediction Using Feature Engineered Boosting Algorithms
Bakhtyari, Mohamadreza
Mirzaei, Saye
[J]. 2021 26TH INTERNATIONAL COMPUTER CONFERENCE, COMPUTER SOCIETY OF IRAN (CSICC), 2021,
[39] Click-through Prediction for Advertising in Twitter Timeline
Li, Cheng
Lu, Yue
Mei, Qiaozhu
Wang, Dong
Pandey, Sandeep
[J]. KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2015, : 1959 - 1968
[40] Click-through rate prediction based on mobile computing and big data analysis
Liu Y.
Pang L.
Lu X.
[J]. Ingenierie des Systemes d'Information, 2019, 24 (03): : 313 - 319

← 1 2 3 4 5 →