Search (31 results, page 2 of 2)

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Abstract

Query reformulation is a key user behavior during Web search. Our research goal is to develop predictive models of query reformulation during Web searching. This article reports results from a study in which we automatically classified the query-reformulation patterns for 964,780 Web searching sessions, composed of 1,523,072 queries, to predict the next query reformulation. We employed an n-gram modeling approach to describe the probability of users transitioning from one query-reformulation state to another to predict their next state. We developed first-, second-, third-, and fourth-order models and evaluated each model for accuracy of prediction, coverage of the dataset, and complexity of the possible pattern set. The results show that Reformulation and Assistance account for approximately 45% of all query reformulations; furthermore, the results demonstrate that the first- and second-order models provide the best predictability, between 28 and 40% overall and higher than 70% for some patterns. Implications are that the n-gram approach can be used for improving searching systems and searching assistance.

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Abstract

Jansen and Pooch review three major search engine studies and compare them to three traditional search system studies and three OPAC search studies, to determine if user search characteristics differ. The web search engine studies indicate that most searchers use two, two search term queries per session, no boolean operators, and look only at the top ten items returned, while reporting the location of relevant information. In traditional search systems we find seven to 16 queries of six to nine terms, while about ten documents per session were viewed. The OPAC studies indicated two to five queries per session of two or less terms, with Boolean search about 1% and less than 50 documents viewed.

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Abstract

Detecting query reformulations within a session by a Web searcher is an important area of research for designing more helpful searching systems and targeting content to particular users. Methods explored by other researchers include both qualitative (i.e., the use of human judges to manually analyze query patterns on usually small samples) and nondeterministic algorithms, typically using large amounts of training data to predict query modification during sessions. In this article, we explore three alternative methods for detection of session boundaries. All three methods are computationally straightforward and therefore easily implemented for detection of session changes. We examine 2,465,145 interactions from 534,507 users of Dogpile.com on May 6, 2005. We compare session analysis using (a) Internet Protocol address and cookie; (b) Internet Protocol address, cookie, and a temporal limit on intrasession interactions; and (c) Internet Protocol address, cookie, and query reformulation patterns. Overall, our analysis shows that defining sessions by query reformulation along with Internet Protocol address and cookie provides the best measure, resulting in an 82% increase in the count of sessions. Regardless of the method used, the mean session length was fewer than three queries, and the mean session duration was less than 30 min. Searchers most often modified their query by changing query terms (nearly 23% of all query modifications) rather than adding or deleting terms. Implications are that for measuring searching traffic, unique sessions may be a better indicator than the common metric of unique visitors. This research also sheds light on the more complex aspects of Web searching involving query modifications and may lead to advances in searching tools.

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Abstract

University libraries provide access to thousands of journals and spend millions of dollars annually on electronic resources. With several commercial entities providing these electronic resources, the result can be silo systems and processes to evaluate cost and usage of these resources, making it difficult to provide meaningful analytics. In this research, we examine a subset of journals from a large research library using a web analytics approach with the goal of developing a framework for the analysis of library subscriptions. This foundational approach is implemented by comparing the impact to the cost, titles, and usage for the subset of journals and by assessing the funding area. Overall, the results highlight the benefit of a web analytics evaluation framework for university libraries and the impact of classifying titles based on the funding area. Furthermore, they show the statistical difference in both use and cost among the various funding areas when ranked by cost, eliminating the outliers of heavily used and highly expensive journals. Future work includes refining this model for a larger scale analysis tying metrics to library organizational objectives and for the creation of an online application to automate this analysis.

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Abstract

In this research we investigate the effect of search engine brand on the evaluation of searching performance. Our research is motivated by the large amount of search traffic directed to a handful of Web search engines, even though many have similar interfaces and performance. We conducted a laboratory experiment with 32 participants using a 42 factorial design confounded in four blocks to measure the effect of four search engine brands (Google, MSN, Yahoo!, and a locally developed search engine) while controlling for the quality and presentation of search engine results. We found brand indeed played a role in the searching process. Brand effect varied in different domains. Users seemed to place a high degree of trust in major search engine brands; however, they were more engaged in the searching process when using lesser-known search engines. It appears that branding affects overall Web search at four stages: (a) search engine selection, (b) search engine results page evaluation, (c) individual link evaluation, and (d) evaluation of the landing page. We discuss the implications for search engine marketing and the design of empirical studies measuring search engine performance.

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Abstract

We analyze the transitions from external search, searching on web search engines, to internal search, searching on websites. We categorize 295,571 search episodes composed of a query submitted to web search engines and the subsequent queries submitted to a single website search by the same users. There are a total of 1,136,390 queries from all searches, of which 295,571 are external search queries and 840,819 are internal search queries. We algorithmically classify queries into states and then use n-grams to categorize search patterns. We cluster the searching episodes into major patterns and identify the most commonly occurring, which are: (1) Explorers (43% of all patterns) with a broad external search query and then broad internal search queries, (2) Navigators (15%) with an external search query containing a URL component and then specific internal search queries, and (3) Shifters (15%) with a different, seemingly unrelated, query types when transitioning from external to internal search. The implications of this research are that external search and internal search sessions are part of a single search episode and that online businesses can leverage these search episodes to more effectively target potential customers.

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Abstract

In this research, we investigated whether a learning process has unique information searching characteristics. The results of this research show that information searching is a learning process with unique searching characteristics specific to particular learning levels. In a laboratory experiment, we studied the searching characteristics of 72 participants engaged in 426 searching tasks. We classified the searching tasks according to Anderson and Krathwohl's taxonomy of the cognitive learning domain. Research results indicate that applying and analyzing, the middle two of the six categories, generally take the most searching effort in terms of queries per session, topics searched per session, and total time searching. Interestingly, the lowest two learning categories, remembering and understanding, exhibit searching characteristics similar to the highest order learning categories of evaluating and creating. Our results suggest the view of Web searchers having simple information needs may be incorrect. Instead, we discovered that users applied simple searching expressions to support their higher-level information needs. It appears that searchers rely primarily on their internal knowledge for evaluating and creating information needs, using search primarily for fact checking and verification. Overall, results indicate that a learning theory may better describe the information searching process than more commonly used paradigms of decision making or problem solving. The learning style of the searcher does have some moderating effect on exhibited searching characteristics. The implication of this research is that rather than solely addressing a searcher's expressed information need, searching systems can also address the underlying learning need of the user.

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Abstract

University libraries provide access to thousands of online journals and other content, spending millions of dollars annually on these electronic resources. Providing access to these online resources is costly, and it is difficult both to analyze the value of this content to the institution and to discern those journals that comparatively provide more value. In this research, we examine 1,510 journals from a large research university library, representing more than 40% of the university's annual subscription cost for electronic resources at the time of the study. We utilize a web analytics approach for the creation of a linear regression model to predict usage among these journals. We categorize metrics into two classes: global (journal focused) and local (institution dependent). Using 275 journals for our training set, our analysis shows that a combination of global and local metrics creates the strongest model for predicting full-text downloads. Our linear regression model has an accuracy of more than 80% in predicting downloads for the 1,235 journals in our test set. The implications of the findings are that university libraries that use local metrics have better insight into the value of a journal and therefore more efficient cost content management.