NDLI: Intent models for contextualising and diversifying query suggestions

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Scholarly big data: information extraction and data mining

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Intent models for contextualising and diversifying query suggestions

Content Provider	ACM Digital Library
Author	Ounis, Iadh Kharitonov, Eugene Macdonald, Craig Serdyukov, Pavel
Abstract	The query suggestion or auto-completion mechanisms help users to type less while interacting with a search engine. A basic approach that ranks suggestions according to their frequency in query logs is suboptimal. Firstly, many candidate queries with the same prefix can be removed as redundant. Secondly, the suggestions can also be personalised based on the user's context. These two directions to improve the mechanisms' quality can be in opposition: while the latter aims to promote suggestions that address search intents that a user is likely to have, the former aims to diversify the suggestions to cover as many intents as possible. We introduce a contextualisation framework that utilises a short-term context using the user's behaviour within the current search session, such as the previous query, the documents examined, and the candidate query suggestions that the user has discarded. This short-term context is used to contextualise and diversify the ranking of query suggestions, by modelling the user's information need as a mixture of intent-specific user models. The evaluation is performed offline on a set of approximately 1.0M test user sessions. Our results suggest that the proposed approach significantly improves query suggestions compared to the baseline approach.
Starting Page	2303
Ending Page	2308
Page Count	6
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505661
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Query suggestions Contextualisation Diversity
Content Type	Text
Resource Type	Article

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