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A co-learning framework for learning user search intents from rule-generated training data

Published: 19 July 2010 Publication History

Abstract

Learning to understand user search intents from their online behaviors is crucial for both Web search and online advertising. However, it is a challenging task to collect and label a sufficient amount of high quality training data for various user intents such as "compare products", "plan a travel", etc. Motivated by this bottleneck, we start with some user common sense, i.e. a set of rules, to generate training data for learning to predict user intents. The rule-generated training data are however hard to be used since these data are generally imperfect due to the serious data bias and possible data noises. In this paper, we introduce a Co-learning Framework (CLF) to tackle the problem of learning from biased and noisy rule-generated training data. CLF firstly generates multiple sets of possibly biased and noisy training data using different rules, and then trains the individual user search intent classifiers over different training datasets independently. The intermediate classifiers are then used to categorize the training data themselves as well as the unlabeled data. The confidently classified data by one classifier are added to other training datasets and the incorrectly classified ones are instead filtered out from the training datasets. The algorithmic performance of this iterative learning procedure is theoretically guaranteed.

Reference

[1]
Russell, D.M., Tang, D., Kellar, M. and Jeffries, R. 2009. Task behaviors during web search: the difficulty of assigning labels. Proceedings of the 42nd Hawaii International Conference on System Sciences (Hawaii, United States, January 05 -- 08, 2009). HICSS '09. IEEE Press, 1--5. DOI= 10.1109/HICSS.2009.417.

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  1. A co-learning framework for learning user search intents from rule-generated training data

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    cover image ACM Conferences
    SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
    July 2010
    944 pages
    ISBN:9781450301534
    DOI:10.1145/1835449
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Published: 19 July 2010

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    Author Tags

    1. classification
    2. search engine
    3. user intent

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    SIGIR '10 Paper Acceptance Rate 87 of 520 submissions, 17%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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