Task-aware query recommendation

Feild, Henry; Allan, James

doi:10.1145/2484028.2484069

Cited by 40 publications

(31 citation statements)

References 18 publications

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“…Some approaches, such as pseudo-relevance feedback [13] and query expansion [30] focus on improving IR systems and algorithms. Others try to improve users' experience by o↵ering related queries [2] or making personalized recommendations [29]. In the end, the e↵ectiveness of an IR setting has to do with user's preferences and/or skills just as much as it has with system performance.…”

Section: Introductionmentioning

confidence: 99%

User-driven system-mediated collaborative information retrieval

Soulier

Shah

Tamine

2014

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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Most of the previous approaches surrounding collaborative information retrieval (CIR) provide either a user-based mediation, in which the system only supports users' collaborative activities, or a system-based mediation, in which the system plays an active part in balancing user roles, reranking results, and distributing them to optimize overall retrieval performance. In this paper, we propose to combine both of these approaches by a role mining methodology that learns from users' actions about the retrieval strategy they adapt. This hybrid method aims at showing how users are di↵erent and how to use these di↵erences for suggesting roles. The core of the method is expressed as an algorithm that (1) monitors users' actions in a CIR setting; (2) discovers di↵erences among the collaborators along certain dimensions; and (3) suggests appropriate roles to make the most out of individual skills and optimize IR performance. Our approach is empirically evaluated and relies on two di↵erent laboratory studies involving 70 pairs of users. Our experiments show promising results that highlight how role mining could optimize the collaboration within a search session. The contributions of this work include a new algorithm for mining user roles in collaborative IR, an evaluation methodology, and a new approach to improve IR performance with the operationalization of user-driven system-mediated collaboration.

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Section: Introductionmentioning

confidence: 99%

User-driven system-mediated collaborative information retrieval

Soulier

Shah

Tamine

2014

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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“…No difference in session success was found between those using the feedback and those not using it, but those using it did continue to employ it effectively on an ongoing basis. A number of recent studies have shown that search tasks provide a rich context for performing log-based query suggestion [64,111,128], underscoring the importance of studying query reformulation in search tasks.…”

Section: Query Reformulation and Refinementmentioning

confidence: 99%

“…Increasingly, these IR systems are able to leverage insights about the context of an information need beyond or even before an expressed query. In such a pro-active search scenario, the context may be given by a user's location [22] or activities [60,64,128]. These new interaction scenarios signal a move from pulling to pushing information, accelerated by constant access to the web on smart phones and tablets.…”

Section: Introductionmentioning

confidence: 99%

Context & Semantics in News & Web Search

Odijk

2016

SIGIR Forum

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This thesis presents research towards a core aim of information retrieval (IR): providing users with easy access to information. Three research themes guide the research presented in this thesis, contributing to three aspects of IR research: the domain in which an IR system is used, the users interacting with the system, and the different access scenarios in which these users engage with an IR system. Central to these research themes is the aim to gain insights into the behavior of searchers and develop algorithms to support them in their quest, whether it is a researcher exploring or studying a large collection, a web searcher struggling to find something, or a television viewer searching for related content. The first research theme is motivated by the information seeking tasks of researchers exploring and studying large collections. To enable their search on a larger scale, we propose computational methods to connect collections and to infer the perspective offered in a news story. Motivated by how historians select documents for close reading, we propose novel methods for connecting collections using automatically extracted temporal references. To illustrate how these algorithms can be used to automatically create connections between collections, we introduce a novel search interface to explore and analyze the connected collections. The interface highlights different perspectives and requires little domain knowledge. Based on how communication scientists study framing in news, we propose an automatic thematic content analysis approach. The second research theme is addressed in a mixed-methods study on how web searchers behave when they cannot find what they are looking for. Based on large-scale log analysis, crowd-sourced labeling, and predictive modeling we show behavioral differences given task success and failure. Based on these findings we propose ways in which systems can reduce struggling in search. To support searchers, we propose and evaluate algorithms that accurately predict the nature of future actions and their anticipated impact on search outcomes. Our findings have implications for the design of search systems that help searchers struggle less and succeed more. In the third and final research theme, we consider a pro-active search scenario, specifically in a live television setting. We propose algorithms that leverage contextual information to retrieve diverse related content for a leaned-back TV viewer. While watching television, people increasingly consume additional content related to what they are watching. Two methods to automatically retrieve content based on subtitles are introduced, one using entity linking, and one that uses reinforcement learning to generate effective queries for finding related content. Both methods are highly efficient and are currently used in a live television setting in near real time. Each research chapter in this thesis provides insights and algorithms that help searchers when using IR applications. For varying domains, users, and access scenarios, the research presented in this thesis improves the ease of access to information.

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“…Research has found that people may be involved in off-topic tasks while working on one-topic tasks (Feild & Allan, 2013), where search is a changing process that combines keyword search, browsing, and serendipity or unintentional discovery (Jiang, He, & Allan, 2014), in addition to impulse purchasing triggered by advertisement banners and promotions that are common in product search activities.…”

Section: Research Papermentioning

confidence: 99%

Identification and Analysis of Multi-tasking Product Information Search Sessions with Query Logs

Zhou

Zhang

Wang

2016

Journal of Data and Information Science

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Purpose: This research aims to identify product search tasks in online shopping and analyze the characteristics of consumer multi-tasking search sessions.Design/methodology/approach: The experimental dataset contains 8,949 queries of 582 users from 3,483 search sessions. A sequential comparison of the Jaccard similarity coefficient between two adjacent search queries and hierarchical clustering of queries is used to identify search tasks.Findings: (1) Users issued a similar number of queries (1.43 to 1.47) with similar lengths (7.3-7.6 characters) per task in mono-tasking and multi-tasking sessions, and (2) Users spent more time on average in sessions with more tasks, but spent less time for each task when the number of tasks increased in a session. Research limitations:The task identification method that relies only on query terms does not completely reflect the complex nature of consumer shopping behavior. Practical implications:These results provide an exploratory understanding of the relationships among multiple shopping tasks, and can be useful for product recommendation and shopping task prediction. Originality/value:The originality of this research is its use of query clustering with online shopping task identification and analysis, and the analysis of product search session characteristics.

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Task-aware query recommendation

Cited by 40 publications

References 18 publications

User-driven system-mediated collaborative information retrieval

User-driven system-mediated collaborative information retrieval

Context & Semantics in News & Web Search

Identification and Analysis of Multi-tasking Product Information Search Sessions with Query Logs

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