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  1. Proceedings of the 2009 workshop on Web Search Click Data (WSCD '09)
  2. Incremental learning to rank with partially-labeled 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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Incremental learning to rank with partially-labeled data

Content Provider ACM Digital Library
Author Kim, Kye-Hyeon Choi, Seungjin
Abstract In this paper we present a semi-supervised learning method for a problem of learning to rank where we exploit Markov random walks and graph regularization in order to incorporate not only "labeled" web pages but also plenty of "un-labeled" web pages (click logs of which are not given) into learning a ranking function. In order to cope with scalability which existing semi-supervised learning methods suffer from, we develop a scalable and incremental method for semi-supervised learning to rank. In the graph regularization framework, we first determine features which well reflects data manifold and then make use of them to train a linear ranking function. We introduce a matrix-fee technique where we compute the eigenvectors of a huge similarity matrix without constructing the matrix itself. Then we present an incremental algorithm to learn a linear ranking function using features determined by projecting data onto the eigenvectors of the similarity matrix, which can be applied to a task of web-scale ranking. We evaluate our method on Live Search query log, showing that search performance is much improved when Live Search yields unsatisfactory search results.
Starting Page 20
Ending Page 27
Page Count 8
File Format PDF
ISBN 9781605584348
DOI 10.1145/1507509.1507513
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-02-09
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Incremental learning Click-through data Semi-supervised learning Information retrieval Learning to rank Web search
Content Type Text
Resource Type Article
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