NDLI: Context-aware query suggestion by mining click-through and session data

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Context-aware query suggestion by mining click-through and session data

Content Provider	ACM Digital Library
Author	Jiang, Daxin Chen, Enhong Li, Hang Cao, Huanhuan He, Qi Pei, Jian Liao, Zhen
Abstract	Query suggestion plays an important role in improving the usability of search engines. Although some recently proposed methods can make meaningful query suggestions by mining query patterns from search logs, none of them are context-aware - they do not take into account the immediately preceding queries as context in query suggestion. In this paper, we propose a novel context-aware query suggestion approach which is in two steps. In the offine model-learning step, to address data sparseness, queries are summarized into concepts by clustering a click-through bipartite. Then, from session data a concept sequence suffix tree is constructed as the query suggestion model. In the online query suggestion step, a user's search context is captured by mapping the query sequence submitted by the user to a sequence of concepts. By looking up the context in the concept sequence sufix tree, our approach suggests queries to the user in a context-aware manner. We test our approach on a large-scale search log of a commercial search engine containing 1:8 billion search queries, 2:6 billion clicks, and 840 million query sessions. The experimental results clearly show that our approach outperforms two baseline methods in both coverage and quality of suggestions.
Starting Page	875
Ending Page	883
Page Count	9
File Format	PDF
ISBN	9781605581934
DOI	10.1145/1401890.1401995
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Session data Click-through data Query suggestion
Content Type	Text
Resource Type	Article

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