The Experts below are selected from a list of 54429 Experts worldwide ranked by ideXlab platform
Alejandro Jaimes - One of the best experts on this subject based on the ideXlab platform.
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Search Behaviour on photo sharing platforms
International Conference on Multimedia and Expo, 2013Co-Authors: Silviu Maniu, Neil Ohare, Luca Maria Aiello, Luca Chiarandini, Alejandro JaimesAbstract:The Behaviour, goals, and intentions of users while Searching for images in large scale online collections are not well understood, with image Search log analysis providing limited insights, in part because they tend only to have access to user Search and result click information. In this paper we study user Search Behaviour in a large photo-sharing platform, analyzing all user actions during Search sessions (i.e. including post result-click pageviews). Search accounts for a significant part of user interactions with such platforms, and we show differences between the queries issued on such platforms and those on general image Search. We show that Search Behaviour is influenced by the query type, and also depends on the user. Finally, we analyse how users behave when they reformulate their queries, and develop URL class prediction models for image Search, showing that query-specific models significantly outperform query-agnostic models. The insights provided in this paper are intended as a launching point for the design of better interfaces and ranking models for image Search.
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ICME - Search Behaviour on photo sharing platforms
2013 IEEE International Conference on Multimedia and Expo (ICME), 2013Co-Authors: Silviu Maniu, Luca Maria Aiello, Luca Chiarandini, Neil O'hare, Alejandro JaimesAbstract:The Behaviour, goals, and intentions of users while Searching for images in large scale online collections are not well understood, with image Search log analysis providing limited insights, in part because they tend only to have access to user Search and result click information. In this paper we study user Search Behaviour in a large photo-sharing platform, analyzing all user actions during Search sessions (i.e. including post result-click pageviews). Search accounts for a significant part of user interactions with such platforms, and we show differences between the queries issued on such platforms and those on general image Search. We show that Search Behaviour is influenced by the query type, and also depends on the user. Finally, we analyse how users behave when they reformulate their queries, and develop URL class prediction models for image Search, showing that query-specific models significantly outperform query-agnostic models. The insights provided in this paper are intended as a launching point for the design of better interfaces and ranking models for image Search.
Peter Bos - One of the best experts on this subject based on the ideXlab platform.
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Social capital and job Search Behaviour of long-term welfare recipients
International Journal of Sociology and Social Policy, 2015Co-Authors: I. Varekamp, Trudie Knijn, Martin Van Der Gaag, Peter BosAbstract:Purpose – Long-term welfare recipients in the Netherlands are either long-term unemployed or part-time employed in jobs that generate incomes below the subsistence level. The question is whether reintegration policies aiming at their return to – a fulltime – job should consider individual social network factors besides psychological and human capital factors. The purpose of this paper is to investigate welfare recipients’ job Search Behaviour, in particular how individual social capital is distributed, and whether it is related to job Search activities. Design/methodology/approach – Standardised and structured interviews were conducted with 189 long-term unemployed welfare recipients. An adapted version of the Resource Generator instrument was used to measure individual access to social capital. Findings – Social capital scales measuring domestic social resources, status-related social resources, expert advice on regulations and financial matters, and advice on finding a job were developed and psychometri...
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Psychosocial Factors Predicting Job Search Behaviour of Long-Term Welfare Recipients in the Netherlands:
European Journal of Social Security, 2014Co-Authors: I. Varekamp, Trudie Knijn, Peter Bos, Frits Van WelAbstract:In spite of active labour market policies, a considerable number of welfare recipients in the Netherlands are long-term unemployed. In order to investigate the job Search Behaviour of this group, w...
Elaine G Toms - One of the best experts on this subject based on the ideXlab platform.
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understanding engagement through Search Behaviour
Conference on Information and Knowledge Management, 2017Co-Authors: Mengdie Zhuang, Gianluca Demartini, Elaine G TomsAbstract:Evaluating user engagement with Search is a critical aspect of understanding how to assess and improve information retrieval systems. While standard techniques for measuring user engagement use questionnaires, these are obtrusive to user interaction, and can only be collected at acceptable intervals. The problem we address is whether there is a less obtrusive and more automatic way to assess how users perceive the Search process and outcome. Log files collect Behavioural signals (e.g., clicks, queries) from users on a large scale. In this paper, we investigate the potential to predict how users perceive engagement with Search by modelling Behavioural signals from log files using supervised learning methods. We focus on different engagement dimensions (Perceived Usability, Felt Involvement, Endurability and Novelty) and examine how 37 Behavioural features can inform these dimensions. Our results, obtained from 377 in-lab participants undergoing goal-based Search tasks, support the connection between perceived engagement and Search Behaviour. More specifically, we show that time- and query-related features are best suited for predicting user perceived engagement, and suggest that different Behavioural features better reflect specific dimensions. We demonstrate the possibility of predicting user-perceived engagement using Search Behavioural features.
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CIKM - Understanding Engagement through Search Behaviour
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017Co-Authors: Mengdie Zhuang, Gianluca Demartini, Elaine G TomsAbstract:Evaluating user engagement with Search is a critical aspect of understanding how to assess and improve information retrieval systems. While standard techniques for measuring user engagement use questionnaires, these are obtrusive to user interaction, and can only be collected at acceptable intervals. The problem we address is whether there is a less obtrusive and more automatic way to assess how users perceive the Search process and outcome. Log files collect Behavioural signals (e.g., clicks, queries) from users on a large scale. In this paper, we investigate the potential to predict how users perceive engagement with Search by modelling Behavioural signals from log files using supervised learning methods. We focus on different engagement dimensions (Perceived Usability, Felt Involvement, Endurability and Novelty) and examine how 37 Behavioural features can inform these dimensions. Our results, obtained from 377 in-lab participants undergoing goal-based Search tasks, support the connection between perceived engagement and Search Behaviour. More specifically, we show that time- and query-related features are best suited for predicting user perceived engagement, and suggest that different Behavioural features better reflect specific dimensions. We demonstrate the possibility of predicting user-perceived engagement using Search Behavioural features.
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SAL@SIGIR - Search Behaviour before and after Search success
2016Co-Authors: Mengdie Zhuang, Elaine G Toms, Gianluca DemartiniAbstract:Why do users continue Searching after reviewing all relevant documents with which they could have completed a work task? If we knew the answer, then a Search system may be able to help users learn about their current Search processes, which in turn may enable them to make the whole Search process more efficient, leading to greater effectiveness and user satisfaction. This paper is a first step towards solving this problem. Using a previously collected data set, we identified the point of success and hence task completion, and investigated the Search Behaviour before and after users had accessed all relevant documents for answering assigned tasks. We used a set of Search Behaviour actions derived from Marchionini's (1995) Information Seeking Process model, and modeled the distribution of these actions throughout the entire Search process, comparing actions before and after success could have been attained. Our results suggest that six defined actions, namely user-submitted query, system-suggested query, forward to items, evaluate relevant items, reflect, and answer appeared to change according to the stage of the entire Search process. Also, users have notably distinct patterns before and after Search success was obtained, but not realised by the user. Not all action were affected; user-submitted query and system-suggested query appeared to be unaffected by time in post-success case and presuccess case, respectively.
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enterprise Search Behaviour of software engineers
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006Co-Authors: Luanne Freund, Elaine G TomsAbstract:Technical professionals spend ~25% of their time at work Searching for information, and have specialized information needs that are not well-served by generic enterprise Search tools. In this study, we investigated how a group of software engineers use a workplace Search system. We identify patterns of Search Behaviour specific to this group and distinct from general web and intranet Search patterns, and make design recommendations for Search systems that will better serve the needs of this group.
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SIGIR - Enterprise Search Behaviour of software engineers
Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '06, 2006Co-Authors: Luanne Freund, Elaine G TomsAbstract:Technical professionals spend ~25% of their time at work Searching for information, and have specialized information needs that are not well-served by generic enterprise Search tools. In this study, we investigated how a group of software engineers use a workplace Search system. We identify patterns of Search Behaviour specific to this group and distinct from general web and intranet Search patterns, and make design recommendations for Search systems that will better serve the needs of this group.
I. Varekamp - One of the best experts on this subject based on the ideXlab platform.
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Social capital and job Search Behaviour of long-term welfare recipients
International Journal of Sociology and Social Policy, 2015Co-Authors: I. Varekamp, Trudie Knijn, Martin Van Der Gaag, Peter BosAbstract:Purpose – Long-term welfare recipients in the Netherlands are either long-term unemployed or part-time employed in jobs that generate incomes below the subsistence level. The question is whether reintegration policies aiming at their return to – a fulltime – job should consider individual social network factors besides psychological and human capital factors. The purpose of this paper is to investigate welfare recipients’ job Search Behaviour, in particular how individual social capital is distributed, and whether it is related to job Search activities. Design/methodology/approach – Standardised and structured interviews were conducted with 189 long-term unemployed welfare recipients. An adapted version of the Resource Generator instrument was used to measure individual access to social capital. Findings – Social capital scales measuring domestic social resources, status-related social resources, expert advice on regulations and financial matters, and advice on finding a job were developed and psychometri...
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Psychosocial Factors Predicting Job Search Behaviour of Long-Term Welfare Recipients in the Netherlands:
European Journal of Social Security, 2014Co-Authors: I. Varekamp, Trudie Knijn, Peter Bos, Frits Van WelAbstract:In spite of active labour market policies, a considerable number of welfare recipients in the Netherlands are long-term unemployed. In order to investigate the job Search Behaviour of this group, w...
Silviu Maniu - One of the best experts on this subject based on the ideXlab platform.
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Search Behaviour on photo sharing platforms
International Conference on Multimedia and Expo, 2013Co-Authors: Silviu Maniu, Neil Ohare, Luca Maria Aiello, Luca Chiarandini, Alejandro JaimesAbstract:The Behaviour, goals, and intentions of users while Searching for images in large scale online collections are not well understood, with image Search log analysis providing limited insights, in part because they tend only to have access to user Search and result click information. In this paper we study user Search Behaviour in a large photo-sharing platform, analyzing all user actions during Search sessions (i.e. including post result-click pageviews). Search accounts for a significant part of user interactions with such platforms, and we show differences between the queries issued on such platforms and those on general image Search. We show that Search Behaviour is influenced by the query type, and also depends on the user. Finally, we analyse how users behave when they reformulate their queries, and develop URL class prediction models for image Search, showing that query-specific models significantly outperform query-agnostic models. The insights provided in this paper are intended as a launching point for the design of better interfaces and ranking models for image Search.
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ICME - Search Behaviour on photo sharing platforms
2013 IEEE International Conference on Multimedia and Expo (ICME), 2013Co-Authors: Silviu Maniu, Luca Maria Aiello, Luca Chiarandini, Neil O'hare, Alejandro JaimesAbstract:The Behaviour, goals, and intentions of users while Searching for images in large scale online collections are not well understood, with image Search log analysis providing limited insights, in part because they tend only to have access to user Search and result click information. In this paper we study user Search Behaviour in a large photo-sharing platform, analyzing all user actions during Search sessions (i.e. including post result-click pageviews). Search accounts for a significant part of user interactions with such platforms, and we show differences between the queries issued on such platforms and those on general image Search. We show that Search Behaviour is influenced by the query type, and also depends on the user. Finally, we analyse how users behave when they reformulate their queries, and develop URL class prediction models for image Search, showing that query-specific models significantly outperform query-agnostic models. The insights provided in this paper are intended as a launching point for the design of better interfaces and ranking models for image Search.
