Predicting short-term interests using activity-based search context
Predicting short-term interests using activity-based search context
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DOI：
10.1145/1871437.1871565
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发表时间：
2010-10
期刊：
影响因子：
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通讯作者：
Ryen W. White;Paul N. Bennett;S. Dumais
中科院分区：
文献类型：
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作者：
Ryen W. White;Paul N. Bennett;S. Dumais
单独考虑的查询提供了关于查询者意图的有限信息。考虑预查询活动的查询上下文（例如，先前的查询和页面访问）可以提供关于搜索意图的更丰富的信息。在本文中，我们描述了一项研究，在该研究中，我们开发和评估用户的兴趣模型，为当前的查询，它的上下文（从预查询会话活动），以及它们的组合，我们称之为意图。使用大规模的日志，我们评估如何准确地每个模型预测用户的短期利益在各种实验条件下。在我们的研究中，我们：（i）确定使用上下文来建模意图的机会的程度;（ii）比较用于构建预测兴趣模型的不同行为证据源（查询、搜索结果点击和网页访问）的效用;以及（iii）通过学习预测每个查询的上下文权重的模型来研究最佳组合查询及其上下文。我们的研究结果表明，在利用上下文信息的重大机会，显示上下文和源影响预测的准确性，并表明，我们可以学习一个接近最佳的组合的查询和上下文的每个查询。这些发现可以为搜索系统的设计提供信息，这些搜索系统利用上下文信息来更好地理解、建模和服务搜索者的信息需求。
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.