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  1. Proceedings of the 2009 workshop on Web Search Click Data (WSCD '09)
  2. Search shortcuts using click-through data
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Survey and evaluation of query intent detection methods
Analysis of long queries in a large scale search log
Distinguishing humans from robots in web search logs: preliminary results using query rates and intervals
Incremental learning to rank with partially-labeled data
Generating unambiguous URL clusters from web search
Topic-specific analysis of search queries
Optimising topical query decomposition
Search shortcuts using click-through data
Query suggestions using query-flow graphs
Using query logs and click data to create improved document descriptions
Intentional query suggestion: making user goals more explicit during search
Usefulness of quality click-through data for training
Comparative analysis of clicks and judgments for IR evaluation
Tailoring click models to user goals

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Search shortcuts using click-through data

Content Provider ACM Digital Library
Author Formoso, Vreixo Carneiro, Victor Silvestri, Fabrizio Perego, Raffaele Baraglia, Ranieri Cacheda, Fidel
Abstract Major Web Search Engines take as a common practice to provide Suggestions to users in order to enhance their search experience. Such suggestions have normally the form of queries that are, to some extent, "similar" to the queries already submitted by the same or related users. The final aim of query suggestions is typically to help users to satisfy their information needs more quickly. In this paper we face this problem from a somewhat different perspective, and we propose a new query suggestion model based on Search Shortcuts, that consist in finding and proposing to the user "Successful" queries that allowed, in the past, several users to satisfy their information needs. This model differs from traditional query suggestion approaches, and allows the evaluation to be performed effectively by exploiting actual user sessions from the Microsoft 2006 RFP dataset. We evaluate several algorithms applied to this problem, both traditional Collaborative Filtering techniques and ad-hoc solutions, and report on preliminary results achieved.
Starting Page 48
Ending Page 55
Page Count 8
File Format PDF
ISBN 9781605584348
DOI 10.1145/1507509.1507517
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-02-09
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Evaluation Search shortcut Model
Content Type Text
Resource Type Article
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