Search (69 results, page 4 of 4)

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Date

20. 1.2015 18:30:22

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Abstract

This study investigated the effects of subject knowledge and search experience on novices' and experienced searchers' use of search tactics in online searches. Novice and experienced searchers searched a practice question and two test questions in the ERIC database on the DIALOG system and their use of search tactics were recorded by protocols, transaction logs, and observation. Search tactics were idetified from the literature and verified in 10 pretests, and nine search tactics variables were operationalized to describe the differences between the two searcher groups. Data analyses showed that that subject knowledge interacted with search experience, and both variables affected searchers' behavior in four ways: (1) when questions in their subject area were searched, experience affected searchers' use of synonymous terms, monitoring of the search process, and combinations of serch terms; (2) when questions outside their subject areas were searched, experience affected searchers' reliance on their own terminology, use of the thesaurus, offline term selection, use of synonymous terms, and combinations of search terms; (3) within the same experience group, subject knowledge had no effect on novice searchers; but (4) subject knowledge affected experienced searcher's reliance on their own language, use of the thesaurus, offline term selection, use of synonymous terms, monitoring of the search, and combinations of search terms. The results showed that search experience affected searchers' use of many search tactics, and suggested that subject knowledge became a factor only after searchers have had a certain amount of search experience

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Abstract

We report on a study that was undertaken to better understand search and navigation behavior by exploiting the close association between the process underlying users' query submission and the navigational trails emanating from query clickthroughs. To our knowledge, there has been little research towards bridging the gap between these two important processes pertaining to users' online information searching activity. Based an log data obtained from a search and navigation documentation system called AutoDoc, we propose a model of user search sessions and provide analysis an users' link or clickthrough selection behavior, reformulation activities, and search strategy patterns. We also conducted a simple user study to gauge users' perceptions of their information seeking activity when interacting with the system. The results obtained show that analyzing both the query submissions and navigation starting from query clickthrough, reveals much more interesting patterns than analyzing these two processes independently. On average, AutoDoc users submitted only one query per search session and entered approximately two query terms. Specifically, our results show how AutoDoc users are more inclined to submit new queries or resubmit modified queries than to navigate by linkfollowing. We also show that users' behavior within this search system can be approximated by Zipf's Law distribution.

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Abstract

Recent studies suggest that the wide variability in type, detail, and reliability of online information motivate expert searchers to develop procedural search knowledge. In contrast to prior research that has focused an finding relevant sources, procedural search knowledge focuses an how to order multiple relevant sources with the goal of retrieving comprehensive information. Because such procedural search knowledge is neither spontaneously inferred from the results of search engines, nor from the categories provided by domainspecific portals, the lack of such knowledge leads most novice searchers to retrieve incomplete information. In domains like healthcare, such incomplete information can lead to dangerous consequences. To address the above problem, a new kind of domain portal called a Strategy Hub was developed and tested. Strategy Hubs provide critical search procedures and associated high-quality links to enable users to find comprehensive and accurate information. We begin by describing how we collaborated with physicians to systematically identify generalizable search procedures to find comprehensive information about a disease, and how these search procedures were made available through the Strategy Hub. A controlled experiment suggests that this approach can improve the ability of novice searchers in finding comprehensive and accurate information, when compared to general-purpose search engines and domain-specific portals. We conclude with insights an how to refine and automate the Strategy Hub design, with the ultimate goal of helping users find more comprehensive information when searching in unfamiliar domains.

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Abstract

An important question in information-seeking behavior is where people go for information and why information seekers prefer to use one source type rather than another when faced with an information-seeking task or need for information. Prior studies have paid little attention to contingent variables that could change the cost-benefit calculus in source use. They also defined source use in one way or the other, or considered source use as a monolithic construct. Through an empirical survey of 352 working professionals in Singapore, this study carried out a context-based investigation into source use by information seekers. Different measures of source use have been incorporated, and various contextual variables that could affect the use of source types have been identified. The findings suggest that source quality and access difficulty are important antecedents of source use, regardless of the source type. Moreover, seekers place more weight on source quality when the task is important. Other contextual factors, however, are generally less important to source use. Seekers also demonstrate a strong pecking order in the use of source types, with online information and face-to-face being the two most preferred types.

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Abstract

In explicit collaborative search, two or more individuals coordinate their efforts toward a shared goal. Every day, Internet users with similar information needs have the potential to collaborate. However, online search is typically performed in solitude. Existing search systems do not promote explicit collaborations, and collaboration opportunities (collabportunities) are missed. In this article, we describe a method to evaluate the feasibility of transforming these collabportunities into recommendations for explicit collaboration. We developed a technique called pseudocollaboration to evaluate the benefits and costs of collabportunities through simulations. We evaluate the performance of our method using three data sets: (a) data from single users' search sessions, (b) data with collaborative search sessions between pairs of searchers, and (c) logs from a large-scale search engine with search sessions of thousands of searchers. Our results establish when and how collabportunities would significantly help or hinder the search process versus searches conducted individually. The method that we describe has implications for the design and implementation of recommendation systems for explicit collaboration. It also connects system-mediated and user-mediated collaborative search, whereby the system evaluates the likely benefits of collaborating for a search task and helps searchers make more informed decisions on initiating and executing such a collaboration.

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Abstract

Over the past decade, the volume of information available digitally over the Internet has grown enormously. Technical developments in the area of search, such as Google's Page Rank algorithm, have proved so good at serving relevant results that Internet search has become integrated into daily human activity. One can endlessly explore topics of interest simply by querying and reading through the resulting links. Yet, although search engines are well known for providing relevant results based on users' queries, users do not always receive the results they are looking for. Google's Director of Research describes clickstream evidence of frustrated users repeatedly reformulating queries and searching through page after page of results. Given the general quality of search engine results, one must consider the possibility that the frustrated user's query is not effective; that is, it does not describe the essence of the user's interest. Indeed, extensive research into human search behavior has found that humans are not very effective at formulating good search queries that describe what they are interested in. Ideally, the user should simply point to a portion of text that sparked the user's interest, and a system should automatically formulate a search query that captures the essence of the text. In this paper, we describe an implemented system that provides this capability. We first describe how our work differs from existing work in automatic query formulation, and propose a new method for improved quantification of the relevance of candidate search terms drawn from input text using phrase-level analysis. We then propose an implementable method designed to provide relevant queries based on a user's text input. We demonstrate the quality of our results and performance of our system through experimental studies. Our results demonstrate that our system produces relevant search terms with roughly two-thirds precision and recall compared to search terms selected by experts, and that typical users find significantly more relevant results (31% more relevant) more quickly (64% faster) using our system than self-formulated search queries. Further, we show that our implementation can scale to request loads of up to 10 requests per second within current online responsiveness expectations (<2-second response times at the highest loads tested).