Context-sensitive information retrieval using implicit feedback

By Xuehua Shen, Bin Tan, Chengxiang Zhai. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2005

558 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/9ce41d1899d51d0eddcdfa0bf5b879875968419e

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Citations in the corpus

Evaluation of contextual information retrieval effectiveness: overview of issues and research (2010, methodology): “A set of test queries are either picked by the user from the log ﬁle [91,100] or new ones are provided by the user [31].”
Explainable Product Search with a Dynamic Relation Embedding Model (2019, background): “Note that other session information such as previous queries and clicks [51] can also be incorporated into the framework if needed.”
Mining long-term search history to improve search accuracy (2006, methodology): “As shown in [5], queries and clickthrough data in the short-term search history provide implicit feedback that can be used to estimate a more accurate query language model and improve retrieval performance.”
Modeling the impact of short- and long-term behavior on search personalization (2012, methodology): “Search personalization improves retrieval effectiveness by tailoring the ranking of results for individual users based on models of their interests [24][28][29].”
ReQ-ReC: high recall retrieval with query pooling and interactive classification (2014, background): “In a feedback procedure, the retrieval system presents the top-ranked documents to the user and collects back either explicit judgments of these documents or implicit feedback implied by certain actions of the user [9, 19].”
A probabilistic method for inferring preferences from clicks (2011, background): “[25] show that integrating click-through information for query-document pairs into a content-based retrieval system can improve retrieval performance substantially.”
Efficient multiple-click models in web search (2009, background): “…Wang Carnegie Mellon University Microsoft Research Microsoft Research Pittsburgh, PA 15213 fanguo@cs.cmu.edu Redmond, WA 98052 chaoliu@microsoft.com Redmond, WA 98052 ymwang@microsoft.com ABSTRACT Many tasks that leverage web search users implicit feedback rely on a proper and unbiased…”
A large-scale evaluation and analysis of personalized search strategies (2007, background): “[25] noted, users often search for documents to satisfy short-term information needs, which may be inconsistent with general user interests.”
A Deep Look into Neural Ranking Models for Information Retrieval (2019, background): “ improve the retrieval performance. Three types of query context have 35 been explored in the literature: Short-term history: the user past interactions with the system in the current search session [136, 137, 138]. Long-term history: the historical information of the user’s queries that is often us”
A data-oriented survey of context models (2007, background): “Abowd discuss in [2], in the last few years this notion has been considered not simply as a state, but part of a process in which users are involved [18]; thus, sophisticated and general context models have been proposed, to support contextaware applications which use them to (a) adapt interfaces [2”
Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement (2016, background): “Note that search personalization is achieved by integrating the user profile in the query reformulation process [23], query-document matching”
Exploratory Search: Beyond the Query-Response Paradigm (2009, background): “, 2001), or by implicitly mining contextual information from users’ interaction behavior (e.g., Dumais et al., 2004; Kelly and Belkin, 2004; Shen et al., 2005).”
Book Review: Statistical Language Models for Information Retrieval by ChengXiang Zhai (2008, background): “, short-term search history [Shen et al. 05] or long-term search history [Tan et al.”
Situational Context for Ranking in Personal Search (2017, methodology): “Shen et al. [39] proposed context-sensitive language models based on short-term search history and evaluated their models using TREC collections.”