Dynamic query intent mining from a search log stream

Authors:
Yanan Qian;Tetsuya Sakai;Junting Ye;Qinghua Zheng;Cong Li
Affiliations:
MOEKLINNS Lab, Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China;Department of Computer Science and Engineering, Waseda University, Tokyo, Japan;MOEKLINNS Lab, Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China;MOEKLINNS Lab, Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China;MOEKLINNS Lab, Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China
Venue:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Year:
2013

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Abstract

It has long been recognized that search queries are often broad and ambiguous. Even when submitting the same query, different users may have different search intents. Moreover, the intents are dynamically evolving. Some intents are constantly popular with users, others are more bursty. We propose a method for mining dynamic query intents from search query logs. By regarding the query logs as a data stream, we identify constant intents while quickly capturing new bursty intents. To evaluate the accuracy and efficiency of our method, we conducted experiments using 50 topics from the NTCIR INTENT-9 data and additional five popular topics, all supplemented with six-month query logs from a commercial search engine. Our results show that our method can accurately capture new intents with short response time.