Transitive history-based query disambiguation for query reformulation

Filali, Karim; Nair, Anish; Leggetter, Chris

doi:10.1145/1835449.1835647

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…We believe that by focusing on factors such as on-and off-task queries, we struck at the core questions in this space. We leave testing whether the results from these experiments port to real user data to future work, but we believe they will, especially given the results of related studies, such as those conducted by Liao et al [14] and Filali et al [8].…”

Section: Discussionmentioning

confidence: 99%

“…Filali et al [8] presented a probabilistic model for generating rewrites based on an arbitrarily long user search history. Their model interpolates the same-task similarity of a rewrite candidate to the reference query with the average similarity of that candidate to all on-task queries from a user's history, weighted by each query's similarity to the reference query.…”

Section: Related Workmentioning

confidence: 99%

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Task-aware query recommendation

Feild

Allan

2013

Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval

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When generating query recommendations for a user, a natural approach is to try and leverage not only the user's most recently submitted query, or reference query, but also information about the current search context, such as the user's recent search interactions. We focus on two important classes of queries that make up search contexts: those that address the same information need as the reference query (on-task queries), and those that do not (off-task queries). We analyze the effects on query recommendation performance of using contexts consisting of only on-task queries, only off-task queries, and a mix of the two. Using TREC Session Track data for simulations, we demonstrate that ontask context is helpful on average but can be easily overwhelmed when off-task queries are interleaved-a common situation according to several analyses of commercial search logs. To minimize the impact of off-task queries on recommendation performance, we consider automatic methods of identifying such queries using a state of the art search task identification technique. Our experimental results show that automatic search task identification can eliminate the effect of off-task queries in a mixed context.We also introduce a novel generalized model for generating recommendations over a search context. While we only consider query text in this study, the model can handle integration over arbitrary user search behavior, such as page visits, dwell times, and query abandonment. In addition, it can be used for other types of recommendation, including personalized web search.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Task-aware query recommendation

Feild

Allan

2013

Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Does More Context Help? Effects of Context Window and Application Source on Retrieval Performance

Vuong

AndolinaSalvatore

JacucciGiulio

et al. 2021

ACM Trans. Inf. Syst.

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We study the effect of contextual information obtained from a user’s digital trace on Web search performance. Contextual information is modeled using Dirichlet–Hawkes processes (DHP) and used in augmenting Web search queries. The context is captured by monitoring all naturally occurring user behavior using continuous 24/7 recordings of the screen and associating the context with the queries issued by the users. We report a field study in which 13 participants installed a screen recording and digital activity monitoring system on their laptops for 14 days, resulting in data on all Web search queries and the associated context data. A query augmentation (QAug) model was built to expand the original query with semantically related terms. The effects of context window and source were determined by training context models with temporally varying context windows and varying application sources. The context models were then utilized to re-rank the QAug model. We evaluate the context models by using the Web document rankings of the original query as a control condition compared against various experimental conditions: (1) a search context condition in which the context was sourced from search history; (2) a non-search context condition in which the context was sourced from all interactions excluding search history; (3) a comprehensive context condition in which the context was sourced from both search and non-search histories; and (4) an application-specific condition in which the context was sourced from interaction histories captured on a specific application type. Our results indicated that incorporating more contextual information significantly improved Web search rankings as measured by the positions of the documents on which users clicked in the search result pages. The effects and importance of different context windows and application sources, along with different query types are analyzed, and their impact on Web search performance is discussed.

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Exploring Privacy and Personalization in Information Retrieval Applications

Allan

Feild

2013

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Transitive history-based query disambiguation for query reformulation

Cited by 3 publications

References 6 publications

Task-aware query recommendation

Task-aware query recommendation

Does More Context Help? Effects of Context Window and Application Source on Retrieval Performance

Exploring Privacy and Personalization in Information Retrieval Applications

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