Personalizing atypical web search sessions

Eickhoff, Carsten; Collins-Thompson, Kevyn; Bennett, Paul; Dumais, Susan T.

doi:10.1145/2433396.2433434

Cited by 17 publications

(9 citation statements)

References 37 publications

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“…(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) atypical Web search sessions ( Eickhoff, Collins-Thompson, Bennett, & Dumais, 2013 ) are being produced by users with atypical information needs, i.e. those outside their regular areas of expertise (often triggered by external events, such as pending medical treatments, financial deadlines or upcoming vacations); exploring sessions ( Awadallah, White, Dumais, & Wang, 2014 ) are those where users are engaged in an open-ended and multi-faceted information-seeking task to foster learning and discovery; struggling sessions ( Awadallah et al, 2014 ) are those where users are experiencing difficulty locating the required information.…”

Section: Resultsmentioning

confidence: 99%

An ant-colony based approach for real-time implicit collaborative information seeking

Malizia

Olsen

Turchi

et al. 2017

Information Processing & Management

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ant-colony based approach for real-time implicit collaborative information seeking. Information Processing and Management, Elsevier, 2017, 53 (3), pp. a b s t r a c tWe propose an approach based on Swarm Intelligence -more specifically on Ant Colony Optimization (ACO) -to improve search engines' performance and reduce information overload by exploiting collective users ' behavior . We designed and developed three dif-ferent algorithms that employ an ACO-inspired strategy to provide implicit collaborative-seeking features in real time to search engines. The three different algorithms -NaïveR -ank, RandomRank , and SessionRank -leverage on different principles of ACO in order to exploit users ' interactions and provide them with more relevant results . We designed an evaluation experiment employing two widely used standard datasets of query -click logs issued to two major Web search engines . The results demonstrated how each algorithm is suitable to be employed in ranking results of different types of queries depending on users' intent. 1 https://developers.google.com/+/web/+1button/ . 2 The inability to make a decision because of the huge quantity of information obtained by the users.

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Section: Resultsmentioning

confidence: 99%

An ant-colony based approach for real-time implicit collaborative information seeking

Malizia

Olsen

Turchi

et al. 2017

Information Processing & Management

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“…Short-term user information plays the same role as the long-term profile in terms of providing additional information for the system to personalize search results (c.f., Shen, Tan, & Zhai, 2005b), and studies (e.g., Bennett et al, 2012;Eickhoff, Collins-Thompson, Bennett, & Dumais, 2013) have found that combining both could improve personalization performance (more see subsection "RLb. Although user profiling is usually for long-term modeling of a user's constant and general interests and preferences, the user's instant information need that can be learned for short-term modeling is not always stored in a profile.…”

Section: User Modelingmentioning

confidence: 99%

“…However, Bennett et al (2012) found that historic behaviors provided substantial benefits at the start of a search session; short-term session behaviors contribute most gains in an extended search session; and the combination of session and historic behaviors outperformed either alone. Eickhoff et al (2013) characterized and personalized search for atypical search sessions, that is, instances when users diverge from their search profiles to satisfy information needs outside their regular areas of interest. They found that certain topics such as medical information and technical support were much more likely to arise in atypical sessions, along with query features such as increased term count, more unique terms, and more natural language-type terms.…”

Section: Rlb Personalization Using Search Behaviorsmentioning

confidence: 99%

Personalization in text information retrieval: A survey

Liu

Belkin

2019

Asso for Info Science & Tech

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Personalization of information retrieval (PIR) is aimed at tailoring a search toward individual users and user groups by taking account of additional information about users besides their queries. In the past two decades or so, PIR has received extensive attention in both academia and industry. This article surveys the literature of personalization in text retrieval, following a framework for aspects or factors that can be used for personalization. The framework consists of additional information about users that can be explicitly obtained by asking users for their preferences, or implicitly inferred from users' search behaviors. Users' characteristics and contextual factors such as tasks, time, location, etc., can be helpful for personalization. This article also addresses various issues including when to personalize, the evaluation of PIR, privacy, usability, etc. Based on the extensive review, challenges are discussed and directions for future effort are suggested.

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“…For instance, during a session search the user's contextual search preference can be updated based on their observed actions [224]; their physical location [41] or their search history can be used to personalize search results and identify when they are behaving atypically [79]. For instance, during a session search the user's contextual search preference can be updated based on their observed actions [224]; their physical location [41] or their search history can be used to personalize search results and identify when they are behaving atypically [79].…”

Section: Dynamics In Information Retrievalmentioning

confidence: 99%

Dynamic Information Retrieval Modeling

HuiYang

MarcSloan

JunWang

2016

Synthesis Lectures on Information Concepts, Retrieval, and Serv

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Synthesis Lectures on Information Concepts, Retrieval, and Services publishes short books on topics pertaining to information science and applications of technology to information discovery, production, distribution, and management. Potential topics include: data models, indexing theory and algorithms, classification, information architecture, information economics, privacy and identity, scholarly communication, bibliometrics and webometrics, personal information management, human information behavior, digital libraries, archives and preservation, cultural informatics, information retrieval evaluation, data fusion, relevance feedback, recommendation systems, question answering, natural language processing for retrieval, text summarization, multimedia retrieval, multilingual retrieval, and exploratory search. ABSTRACTBig data and human-computer information retrieval (HCIR) are changing IR. ey capture the dynamic changes in the data and dynamic interactions of users with IR systems. A dynamic system is one which changes or adapts over time or a sequence of events. Many modern IR systems and data exhibit these characteristics which are largely ignored by conventional techniques. What is missing is an ability for the model to change over time and be responsive to stimulus. Documents, relevance, users and tasks all exhibit dynamic behavior that is captured in data sets typically collected over long time spans and models need to respond to these changes. Additionally, the size of modern datasets enforces limits on the amount of learning a system can achieve. Further to this, advances in IR interface, personalization and ad display demand models that can react to users in real time and in an intelligent, contextual way.In this book we provide a comprehensive and up-to-date introduction to Dynamic Information Retrieval Modeling, the statistical modeling of IR systems that can adapt to change. We define dynamics, what it means within the context of IR and highlight examples of problems where dynamics play an important role. We cover techniques ranging from classic relevance feedback to the latest applications of partially observable Markov decision processes (POMDPs) and a handful of useful algorithms and tools for solving IR problems incorporating dynamics.e theoretical component is based around the Markov Decision Process (MDP), a mathematical framework taken from the field of Artificial Intelligence (AI) that enables us to construct models that change according to sequential inputs. We define the framework and the algorithms commonly used to optimize over it and generalize it to the case where the inputs aren't reliable. We explore the topic of reinforcement learning more broadly and introduce another tool known as a Multi-Armed Bandit which is useful for cases where exploring model parameters is beneficial. Following this we introduce theories and algorithms which can be used to incorporate dynamics into an IR model before presenting an array of state-of-the-art research that already does, such as in the areas of se...

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Personalizing atypical web search sessions

Cited by 17 publications

References 37 publications

An ant-colony based approach for real-time implicit collaborative information seeking

An ant-colony based approach for real-time implicit collaborative information seeking

Personalization in text information retrieval: A survey

Dynamic Information Retrieval Modeling

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