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Predicting short-term interests using activity-based search context (2010)

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by Ryen W. White
Venue:In CIKM
Citations:40 - 17 self
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BibTeX

@INPROCEEDINGS{White10predictingshort-term,
    author = {Ryen W. White},
    title = {Predicting short-term interests using activity-based search context},
    booktitle = {In CIKM},
    year = {2010},
    pages = {1009--1018}
}

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Abstract

A query considered in isolation offers limited information about a searcher’s intent. Query context that considers pre-query activity (e.g., previous queries and page visits), can provide richer information about search intentions. In this paper, we describe a study in which we developed and evaluated user interest models for the current query, its context (from pre-query session activity), and their combination, which we refer to as intent. Using large-scale logs, we evaluate how accurately each model predicts the user’s short-term interests under various experimental conditions. In our study we: (i) determine the extent of opportunity for using context to model intent; (ii) compare the utility of different sources of behavioral evidence (queries, search result clicks, and Web page visits) for building predictive interest models, and; (iii) investigate optimally combining the query and its context by learning a model that predicts the context weight for each query. Our findings demonstrate significant opportunity in leveraging contextual information, show that context and source influence predictive accuracy, and show that we can learn a near-optimal combination of the query and context for each query. The findings can inform the design of search systems that leverage contextual information to better understand, model, and serve searchers ’ information needs.

Keyphrases

short-term interest    activity-based search context    contextual information    user interest model    user short-term interest    query context    search result click    searcher information need    near-optimal combination    source influence predictive accuracy    previous query    behavioral evidence    current query    different source    large-scale log    various experimental condition    pre-query session activity    isolation offer    search intention    predictive interest model    context weight    search system    pre-query activity    significant opportunity    searcher intent   

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