Human–computer interaction : the impact of users’cognitive styles on query reformulation behaviourduring web searching
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This paper discusses users’ query reformulation behaviour while searching information on the Web. Query reformulations have emerged as an important component of Web search behaviour and human-computer interaction (HCI) because a user’s success of information retrieval (IR) depends on how he or she formulates queries. There are various factors, such as cognitive styles, that influence users’ query reformulation behaviour. Understanding how users with different cognitive styles formulate their queries while performing Web searches can help HCI researchers and information systems (IS) developers to provide assistance to the users. This paper aims to examine the effects of users’ cognitive styles on their query reformation behaviour. To achieve the goal of the study, a user study was conducted in which a total of 3613 search terms and 872 search queries were submitted by 50 users who engaged in 150 scenario-based search tasks. Riding’s (1991) Cognitive Style Analysis (CSA) test was used to assess users’ cognitive style as wholist or analytic, and verbaliser or imager. The study findings show that users’ query reformulation behaviour is affected by their cognitive styles. The results reveal that analytic users tended to prefer Add queries while all other users preferred New queries. A significant difference was found among wholists and analytics in the manner they performed Remove query reformulations. Future HCI researchers and IS developers can utilize the study results to develop interactive and user-cantered search model, and to provide context-based query suggestions for users.Keywords:
Cognitive style
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A general characteristic of Information Retrieval (IR) and Multilingual IR (MIR) [5] systems is that if the same query was submitted by different users, the system would yield the same results, regardless of the user. On the other hand, Adaptive Hypermedia (AH) systems operate in a personalized manner where the services are adapted to the user [1]. Personalized IR (PIR) is motivated by the success in both areas, IR and AH [4]. IR systems have the advantage of scalability and AH systems have the advantage of satisfying individual user needs. The majority of studies in PIR literature have focused on monolingual IR, and relatively little work has been done concerning multilingual IR.
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This paper discusses users' query reformulation behaviour while searching information on the Web. Query reformulations have emerged as an important component of Web search behaviour and human-computer interaction (HCI) because a user's success of information retrieval (IR) depends on how he or she formulates queries. There are various factors, such as cognitive styles, that influence users' query reformulation behaviour. Understanding how users with different cognitive styles formulate their queries while performing Web searches can help HCI researchers and information systems (IS) developers to provide assistance to the users. This paper aims to examine the effects of users' cognitive styles on their query reformation behaviour. To achieve the goal of the study, a user study was conducted in which a total of 3613 search terms and 872 search queries were submitted by 50 users who engaged in 150 scenario-based search tasks. Riding's (1991) Cognitive Style Analysis (CSA) test was used to assess users' cognitive style as wholist or analytic, and verbaliser or imager. The study findings show that users' query reformulation behaviour is affected by their cognitive styles. The results reveal that analytic users tended to prefer Add queries while all other users preferred New queries. A significant difference was found among wholists and analytics in the manner they performed Remove query reformulations. Future HCI researchers and IS developers can utilize the study results to develop interactive and personalised search model, and to provide context-based query suggestions for users.
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The queries submitted by users to search engines often poorly describe their information needs and represent a potential bottleneck in the system. In this paper we investigate to what extent it is possible to aid users in learning how to formulate better queries by providing examples of high-quality queries interactively during a number of search sessions. By means of several controlled user studies we collect quantitative and qualitative evidence that shows: (1) study participants are able to identify and abstract qualities of queries that make them highly effective, (2) after seeing high-quality example queries participants are able to themselves create queries that are highly effective, and, (3) those queries look similar to expert queries as defined in the literature. We conclude by discussing what the findings mean in the context of the design of interactive search systems.
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Along with research on information retrieval and filtering, text summarization is an effective technique to help users save time in finding critical information and making timely decisions. Some existing summarization approaches have used a userpsilas interests to develop a personalized text summarization system. However, there is inadequate focus on exploring cognitive styles, which have been found to affect the ways users think, perceive and remember information. Our main objective of this evaluation is to investigate the effect of a userpsilas cognitive style on multi-document summarization. We examine two dimensions of a userpsilas cognitive style which are the analytic/who list and verbal/imagery dimensions. We conducted an experiment to determine the impact of a userpsilas cognitive style when working with different types of document sets. The type of a document set refers to whether the content of this set is loosely related or closely related. Our results show that users in general are insensitive to the types of document sets both in terms of information covered in a summary as well as the way that a summary is written and presented. However, if we group users by the analytic/who list dimension, we found that people in groups are sensitive to the way that the information is presented for different types of document sets.
Multi-document summarization
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With the rise in the popularity of the Web, more and more users are spending time online. Even though a lot of data is created and consumed by users, we often have limited understanding of how to interpret this data. One of the major challenges in web information retrieval is in accurately understanding the meaning of a keyword in a search query, ad phrase, or in a webpage. The meaning of a term may be different for different users in different contexts. In this research, we study the problem of interpretation of keywords in three increasingly complex settings incorporating more and more information about the user.
In the first part of this thesis, we study the problem of interpreting keywords for a general user in the web search setting. Specifically, we address the challenges in understanding the underlying concepts in general user queries, which, being short and vague, are often difficult to interpret. We observe that if a retrieval system can accurately identify the underlying concept of a query, it can provide a better search experience to the user. We model the problem as a query classification problem, i.e., given a query we want to assign it to a suitable node in a large scale topical taxonomy. By using documents related to the query to add context to the queries, we propose a methodology for building a robust query classification system. This system can identify the underlying topic of a query and assign it to a taxonomy with thousands of query classes. Empirical evaluation confirms that our methodology yields a considerably higher classification accuracy than previously reported.
In the second part of this thesis, we take a step further, to interpret keywords in the context of a certain type of user, as defined by the user's demographic and behavioral profile. In this, we work with the contextual advertising setting. We build a generic user profile with user demographic and behavioral information, and propose methods to incorporate this user profile into an existing ad retrieval model. We show that user information has a clear correlation with click performance in a contextual advertising setting. Based on this correlation, we learn a mapping from user demographic and behavioral features to the text features of pages and ads. This mapping, when incorporated into the conventional advertising system, has shown an improvement in the click rate on ads in an experiment on live traffic at a large ad network.
Finally, in the third part, we study the problem of interpreting keywords for users in a social web setting. Specifically, we look at the problem of vocabulary mismatch between a searcher and a content creator in a tag-based retrieval system. To alleviate this problem, we propose and evaluate a simple, yet effective, concept-driven probabilistic model, and two methods to implement it. We evaluate both techniques for how effectively they capture the intended meaning of a term from the content creator and searcher, and their overall value in improving the search. Our results show that the proposed concept-driven probabilistic model, though simple, clearly outperforms plain search.
Thus, we propose and evaluate methodologies to interpret keywords in increasingly complex settings while leveraging more and more information about the user.
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The exploitation of the underlying semantics of data inherent in the vision of the Semantic Web tackles the limitations of the traditional keywords-based retrieval model and has the ability to change the way search is done. The proliferation of Open Data published on the Web in recent years has driven significant research and development in search. As a result, there is a wide range of approaches with respect to the style of input, the underlying search mechanisms and the manner in which results are presented. Although the performance or effectiveness of these approaches is usually evaluated, understanding their usability and suitability for end users' needs and preferences has been largely overlooked. This is the main motivation behind the work presented in this thesis.
The thesis, thus, presents different pieces of work in this area. The �first part focuses on investigating the usability of different query approaches from the perspective of expert and casual users through a user-based study. The �findings of this study show the strengths of graph-based approaches in supporting users during query formulation with a drawback of high query input time and user effort. Therefore, in another user-based study, learnability of a graph-based approach is evaluated to assess the effects of learning and frequency of use on users' proficiency and satisfaction. The results of both studies suggest that the combination of a graph- based approach with a NL input feature could provide high level of support and satisfaction for users during query formulation. This is, hence, the third piece of work presented in the thesis: a hybrid query approach together with a user-based evaluation to assess its usability and users' satisfaction. The thesis also presents thorough analysis of state-of-the-art in semantic search evaluations and describes a set of best practices for running them based on this analysis, lessons learnt from the Information Retrieval community and on my own experience in evaluating semantic search approaches.
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Semantic Search
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Most Information Retrieval (IR) software is designed to fit a general user where users are submitting queries and the retrieval system returns a ranked list of results. Regardless of the user, the query always returns the same list of results. Individual aspects like age, gender, profession or experience are often not taken into account, for example the difference in searching between children and adults. Although long challenged by works such as Bates' berrypicking model [1], common systems still assume that the user has a static information need which remains unchanged during the seeking process. Moreover many systems are strongly optimized for lookup searches, expecting that the user is only interested in facts and not in complex problem solving.
BATES
Exploratory search
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Search has become a fundamental life activity. An individual looks for meaningful information not only for work-related requests but also for psychological and social satisfaction. A traditional query-result paradigm is able to deliver sensibly for short, navigational and close information requests. It is not always adequate, particularly when user is not familiar with the syntax and semantics of the data, and worse when the user is uncertain of his information needs. Such as the case in discovery-oriented applications e.g. scientific data, genomics, health data etc.. User's intention additionally helps to navigate through the unknown data, formulate queries and find desired information. Also, user's initial search aims and intentions evolve as new information is encountered. To guide users in navigating large datasets in discovery-related applications, we proposed an interactive exploratory search system with visualizations and interactive features to guide the user in recognizing the relationship between query and results retrieved. Proposed system incorporates explicit user feedback to re-rank results according to the user's intent, contrary to traditional systems predefined relevance norms. The usage pattern and usability of various interactions are measured with the help of a suggested assessment framework. Proposed ESS outperforms present list based systems in lowering the cognitive burden, swift interactions, rapid and improved convergence towards search goals.
Exploratory search
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Rank (graph theory)
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Information needs
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Due to both the size and growth of the internet, new tools are needed to assist with the finding and extraction of very specific resources relevant to a user's task. Previously, the definition of relevance has been related to the matching between documents and query terms but recently the emphasis is shifting towards a more personalised model based on the relevance of a particular resource for one specific user. In this paper, we introduce our system, Fetch, which adopts this concept within an information-seeking environment specifically designed to provide users with means to describe a long-term multifaceted information need. By taking advantage of the way in which users bundle together groups of documents representing a particular topic, query languages as we know them can be taken to a higher and more useful level of abstraction. The agent personalises the search experience by using this information to formulate queries with the aim of returning documents relevant to the user's information need. In this paper we report on both qualitative and quantitative aspects of system use based on information collected in the pilot evaluation.
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Abstraction
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