Using Contextual Information to Understand Searching and Browsing Behavior

Kiseleva, Julia

doi:10.1145/2766462.2767852

Cited by 6 publications

(4 citation statements)

References 157 publications

(349 reference statements)

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“…Liu and Wu (2015) suggested that contextual information, which included time, weather, and emotions, influences a user's preference on a certain point of interest. Taking geographical place and user behavior as contextual information, Kiseleva (2015) studied their influence on searching and browsing behavior. Gasimov, Magagna, and Sutanto (2010) divided contextual information into device information, user information, and environment information, and presented a simple architecture for adaptive mobile Web page browsing.…”

Section: Research On the Mobile Search Contextmentioning

confidence: 99%

How Users Search the Mobile Web: A Model for Understanding the Impact of Motivation and Context on Search Behaviors

Zhu

Ran

2016

Journal of Data and Information Science

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Purpose: This study explores how search motivation and context influence mobile Web search behaviors.Design/methodology/approach: We studied 30 experienced mobile Web users via questionnaires, semi-structured interviews, and an online diary tool that participants used to record their daily search activities. SQLite Developer was used to extract data from the users' phone logs for correlation analysis in Statistical Product and Service Solutions (SPSS). Findings:One quarter of mobile search sessions were driven by two or more search motivations. It was especially difficult to distinguish curiosity from time killing in particular user reporting. Multi-dimensional contexts and motivations influenced mobile search behaviors, and among the context dimensions, gender, place, activities they engaged in while searching, task importance, portal, and interpersonal relations (whether accompanied or alone when searching) correlated with each other. Research limitations:The sample was comprised entirely of college students, so our findings may not generalize to other populations. More participants and longer experimental duration will improve the accuracy and objectivity of the research. Practical implications:Motivation analysis and search context recognition can help mobile service providers design applications and services for particular mobile contexts and usages.Originality/value: Most current research focuses on specific contexts, such as studies on place, or other contextual influences on mobile search, and lacks a systematic analysis of mobile search context. Based on analysis of the impact of mobile search motivations and search context on search behaviors, we built a multi-dimensional model of mobile search behaviors.

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Section: Research On the Mobile Search Contextmentioning

confidence: 99%

How Users Search the Mobile Web: A Model for Understanding the Impact of Motivation and Context on Search Behaviors

Zhu

Ran

2016

Journal of Data and Information Science

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“…The importance of contextual information in search applications has been recognised by researchers and practitioners in many disciplines, including recommendation systems, information retrieval, ubiquitous and mobile computing, and marketing. Context-aware systems [20,21] adapt to users operations and thus aim at improving the usability and effectiveness by taking context into account. In this work we consider two types of behavior: (1) 'searching'-when users are issuing queries and we are trying to improve search results (SERP) taking context of sessions into account; and (2) 'browsing'-when users are surfing a website and we are predicting their movements utilizing context.…”

Section: Using Contextual Information To Understand Searching and Bro...mentioning

confidence: 99%

Contextual Search and Exploration

Kiseleva

Kamps

Clarke

2016

Communications in Computer and Information Science

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“…It also provides guidance to the systems' design and evaluation such as deciding where each user interface component should be placed and which content should be provided to the user. Machine learning (ML) techniques have been applied to model user search behaviour by building global user interactions models [32] or models that group users into communities with common interests [17]. ML techniques are promising in cases where very large datasets are available.…”

Section: Introductionmentioning

confidence: 99%

Query Change as a Contextual Markov Model for Simulating User Search Behaviour

Zerhoudi

Schlötterer

Seifert

2021

Forum for Information Retrieval Evaluation

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Search engine users issue queries to formulate their information need and gain useful insights. However, it is challenging for search engines to understand different users' search type intents and return appropriate results. Simulating user search behaviour allows information retrieval systems (IR) to parameterise the a-priori distribution of search types using different back-end configurations and user interface variants to improve the retrieval functionality. In this paper, we propose a formal Markov approach in which we utilise the context discovery process to model user-type specific behaviour by capturing the user's query change in a search session. Contextual Markov models have been used in the past to improve the prediction of user intentions, we investigate here their efficiency in simulating user-type specific interactions. Additionally, we provide an empirical and classification-based evaluation that can be used in simulation assessment. Overall, we report that the proposed approach reliably simulates user-type specific behaviour on a real-world academic search engine log dataset.

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Using Contextual Information to Understand Searching and Browsing Behavior

Cited by 6 publications

References 157 publications

How Users Search the Mobile Web: A Model for Understanding the Impact of Motivation and Context on Search Behaviors

How Users Search the Mobile Web: A Model for Understanding the Impact of Motivation and Context on Search Behaviors

Contextual Search and Exploration

Query Change as a Contextual Markov Model for Simulating User Search Behaviour

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