Implicit queries (IQ) for contextualized search

Dumais, Susan T.; Cutrell, Edward; Sarin, Raman; Horvitz, Eric

doi:10.1145/1008992.1009137

Cited by 46 publications

(37 citation statements)

References 4 publications

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“…In [17], some existing retrieval algorithms are adapted to improve search results based on the browsing history of a user. Other related work on using context includes personalized search [1,3,4,7,10], query log analysis [5], context factors [12], and implicit queries [6].…”

Section: Introductionmentioning

confidence: 99%

Context-sensitive information retrieval using implicit feedback

Shen

Tan

Zhai

2005

Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

330

247

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A major limitation of most existing retrieval models and systems is that the retrieval decision is made based solely on the query and document collection; information about the actual user and search context is largely ignored. In this paper, we study how to exploit implicit feedback information, including previous queries and clickthrough information, to improve retrieval accuracy in an interactive information retrieval setting. We propose several contextsensitive retrieval algorithms based on statistical language models to combine the preceding queries and clicked document summaries with the current query for better ranking of documents. We use the TREC AP data to create a test collection with search context information, and quantitatively evaluate our models using this test set. Experiment results show that using implicit feedback, especially the clicked document summaries, can improve retrieval performance substantially.

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Section: Introductionmentioning

confidence: 99%

Context-sensitive information retrieval using implicit feedback

Shen

Tan

Zhai

2005

Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

330

247

View full text Add to dashboard Cite

show abstract

“…Implicit queries have earlier been constructed based solely on the texts the user is working on (Czerwinski et al 1999;Dumais et al 2004); in this work we combine this research tradition with more focused eye tracking-based inferences on which parts of the document are interesting. This is a feasibility study investigating whether eye tracking gives valuable information in this task.…”

Section: Introductionmentioning

confidence: 99%

Can eyes reveal interest? Implicit queries from gaze patterns

Ajanki

Hardoon

Kaski

et al. 2009

User Model User-Adap Inter

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We study a new research problem, where an implicit information retrieval query is inferred from eye movements measured when the user is reading, and used to retrieve new documents. In the training phase, the user's interest is known, and we learn a mapping from how the user looks at a term to the role of the term in the implicit query. Assuming the mapping is universal, that is, the same for all queries in a given domain, we can use it to construct queries even for new topics for which no learning data is available. We constructed a controlled experimental setting to show that when the system has no prior information as to what the user is searching, the eye movements help significantly in the search. This is the case in a proactive search, for instance, where the system monitors the reading behaviour of the user in a new topic. In contrast, during a search or reading session where the set of inspected documents is biased towards being relevant, a stronger strategy is to search for content-wise similar documents than to use the eye movements.

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“…by emphasizing the words mentioned in the abstract or written with larger fonts, in addition to word frequency. The Implicit Queries (IQ) system [14], [15] generated context-sensitive searches by analyzing the text that a user is reading or composing. IQ automatically identified important words to use in a query using TFIDF weights.…”

Section: A Query Formulation In Just-in-time Retrieval Systemsmentioning

confidence: 99%

Keyword Extraction and Clustering for Document Recommendation in Conversations

Habibi

Popescu-Belis

2015

IEEE/ACM Trans. Audio Speech Lang. Process.

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This paper addresses the problem of keyword extraction from conversations, with the goal of using these keywords to retrieve, for each short conversation fragment, a small number of potentially relevant documents, which can be recommended to participants. However, even a short fragment contains a variety of words, which are potentially related to several topics; moreover, using an automatic speech recognition (ASR) system introduces errors among them. Therefore, it is difficult to infer precisely the information needs of the conversation participants. We first propose an algorithm to extract keywords from the output of an ASR system (or a manual transcript for testing), which makes use of topic modeling techniques and of a submodular reward function which favors diversity in the keyword set, to match the potential diversity of topics and reduce ASR noise. Then, we propose a method to derive multiple topically separated queries from this keyword set, in order to maximize the chances of making at least one relevant recommendation when using these queries to search over the English Wikipedia. The proposed methods are evaluated in terms of relevance with respect to conversation fragments from the Fisher, AMI, and ELEA conversational corpora, rated by several human judges. The scores show that our proposal improves over previous methods that consider only word frequency or topic similarity, and represents a promising solution for a document recommender system to be used in conversations.

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Implicit queries (IQ) for contextualized search

Cited by 46 publications

References 4 publications

Context-sensitive information retrieval using implicit feedback

Context-sensitive information retrieval using implicit feedback

Can eyes reveal interest? Implicit queries from gaze patterns

Keyword Extraction and Clustering for Document Recommendation in Conversations

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