Search (27 results, page 1 of 2)

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Abstract

In this research, we aim to identify factors that significantly affect the clickthrough of Web searchers. Our underlying goal is determine more efficient methods to optimize the clickthrough rate. We devise a clickthrough metric for measuring customer satisfaction of search engine results using the number of links visited, number of queries a user submits, and rank of clicked links. We use a neural network to detect the significant influence of searching characteristics on future user clickthrough. Our results show that high occurrences of query reformulation, lengthy searching duration, longer query length, and the higher ranking of prior clicked links correlate positively with future clickthrough. We provide recommendations for leveraging these findings for improving the performance of search engine retrieval and result ranking, along with implications for search engine marketing.

Date

22. 3.2009 17:49:11

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Abstract

Searching for multimedia is an important activity for users of Web search engines. Studying user's interactions with Web search engine multimedia buttons, including image, audio, and video, is important for the development of multimedia Web search systems. This article provides results from a Weblog analysis study of multimedia Web searching by Dogpile users in 2006. The study analyzes the (a) duration, size, and structure of Web search queries and sessions; (b) user demographics; (c) most popular multimedia Web searching terms; and (d) use of advanced Web search techniques including Boolean and natural language. The current study findings are compared with results from previous multimedia Web searching studies. The key findings are: (a) Since 1997, image search consistently is the dominant media type searched followed by audio and video; (b) multimedia search duration is still short (>50% of searching episodes are <1 min), using few search terms; (c) many multimedia searches are for information about people, especially in audio search; and (d) multimedia search has begun to shift from entertainment to other categories such as medical, sports, and technology (based on the most repeated terms). Implications for design of Web multimedia search engines are discussed.

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Abstract

Major Web search engines, such as AItaVista, are essential tools in the quest to locate online information. This article reports research that used transaction log analysis to examine the characteristics and changes in AItaVista Web searching that occurred from 1998 to 2002. The research questions we examined are (1) What are the changes in AItaVista Web searching from 1998 to 2002? (2) What are the current characteristics of AItaVista searching, including the duration and frequency of search sessions? (3) What changes in the information needs of AItaVista users occurred between 1998 and 2002? The results of our research show (1) a move toward more interactivity with increases in session and query length, (2) with 70% of session durations at 5 minutes or less, the frequency of interaction is increasing, but it is happening very quickly, and (3) a broadening range of Web searchers' information needs, with the most frequent terms accounting for less than 1% of total term usage. We discuss the implications of these findings for the development of Web search engines.

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Abstract

Ecommerce is developing into a fast-growing channel for new business, so a strong presence in this domain could prove essential to the success of numerous commercial organizations. However, there is little research examining ecommerce at the individual customer level, particularly on the success of everyday ecommerce searches. This is critical for the continued success of online commerce. The purpose of this research is to evaluate the effectiveness of search engines in the retrieval of relevant ecommerce links. The study examines the effectiveness of five different types of search engines in response to ecommerce queries by comparing the engines' quality of ecommerce links using topical relevancy ratings. This research employs 100 ecommerce queries, five major search engines, and more than 3540 Web links. The findings indicate that links retrieved using an ecommerce search engine are significantly better than those obtained from most other engines types but do not significantly differ from links obtained from a Web directory service. We discuss the implications for Web system design and ecommerce marketing campaigns.

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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

Purpose - The purpose of this paper is to examine the way in which end user searching on the web has become the primary method of locating digital images for many people. This paper seeks to investigate how users structure these image queries. Design/methodology/approach - This study investigates the structure and formation of image queries on the web by mapping a sample of web queries to three known query classification schemes for image searching (i.e. Enser and McGregor, Jörgensen, and Chen). Findings - The results indicate that the features and attributes of web image queries differ relative to image queries utilized on other information retrieval systems and by other user populations. This research points to the need for five additional attributes (i.e. collections, pornography, presentation, URL, and cost) in order to classify web image queries, which were not present in any of the three prior classification schemes. Research limitations/implications - Patterns in web searching for image content do emerge that inform the design of web-based multimedia systems, namely, that there is a high interest in locating image collections by web searchers. Objects and people images are the predominant interest for web searchers. Cost is a factor for web searching. This knowledge of the structure of web image queries has implications for the design of image information retrieval systems and repositories, especially in the area of automatic tagging of images with metadata. Originality/value - This is the first research that examines whether or not one can apply image query classifications schemes to web image queries.

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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.

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Abstract

In this article, we identify, compare, and contrast theoretical constructs for the fields of information searching and information retrieval to emphasize the uniqueness of and synergy between the fields. Theoretical constructs are the foundational elements that underpin a field's core theories, models, assumptions, methodologies, and evaluation metrics. We provide a framework to compare and contrast the theoretical constructs in the fields of information searching and information retrieval using intellectual perspective and theoretical orientation. The intellectual perspectives are information searching, information retrieval, and cross-cutting; and the theoretical orientations are information, people, and technology. Using this framework, we identify 17 significant constructs in these fields contrasting the differences and comparing the similarities. We discuss the impact of the interplay among these constructs for moving research forward within both fields. Although there is tension between the fields due to contradictory constructs, an examination shows a trend toward convergence. We discuss the implications for future research within the information searching and information retrieval fields.

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Abstract

The Web is a communication and information technology that is often used for the distribution and retrieval of personal information. Many people and organizations mount Web sites containing large amounts of information on individuals, particularly about celebrities. However, limited studies have examined how people search for information on other people, using personal names, via Web search engines. Explores the nature of personal name searching on Web search engines. The specific research questions addressed in the study are: "Do personal names form a major part of queries to Web search engines?"; "What are the characteristics of personal name Web searching?"; and "How effective is personal name Web searching?". Random samples of queries from two Web search engines were analyzed. The findings show that: personal name searching is a common but not a major part of Web searching with few people seeking information on celebrities via Web search engines; few personal name queries include double quotations or additional identifying terms; and name searches on Alta Vista included more advanced search features relative to those on AlltheWeb.com. Discusses the implications of the findings for Web searching and search engines, and further research.

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Abstract

The application of clustering to Web search engine technology is a novel approach that offers structure to the information deluge often faced by Web searchers. Clustering methods have been well studied in research labs; however, real user searching with clustering systems in operational Web environments is not well understood. This article reports on results from a transaction log analysis of Vivisimo.com, which is a Web meta-search engine that dynamically clusters users' search results. A transaction log analysis was conducted on 2-week's worth of data collected from March 28 to April 4 and April 25 to May 2, 2004, representing 100% of site traffic during these periods and 2,029,734 queries overall. The results show that the highest percentage of queries contained two terms. The highest percentage of search sessions contained one query and was less than 1 minute in duration. Almost half of user interactions with clusters consisted of displaying a cluster's result set, and a small percentage of interactions showed cluster tree expansion. Findings show that 11.1% of search sessions were multitasking searches, and there are a broad variety of search topics in multitasking search sessions. Other searching interactions and statistics on repeat users of the search engine are reported. These results provide insights into search characteristics with a cluster-based Web search engine and extend research into Web searching trends.

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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

The Web and especially major Web search engines are essential tools in the quest to locate online information for many people. This paper reports results from research that examines characteristics and changes in Web searching from nine studies of five Web search engines based in the US and Europe. We compare interactions occurring between users and Web search engines from the perspectives of session length, query length, query complexity, and content viewed among the Web search engines. The results of our research shows (1) users are viewing fewer result pages, (2) searchers on US-based Web search engines use more query operators than searchers on European-based search engines, (3) there are statistically significant differences in the use of Boolean operators and result pages viewed, and (4) one cannot necessary apply results from studies of one particular Web search engine to another Web search engine. The wide spread use of Web search engines, employment of simple queries, and decreased viewing of result pages may have resulted from algorithmic enhancements by Web search engine companies. We discuss the implications of the findings for the development of Web search engines and design of online content.

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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

In this paper we report research results investigating microblogging as a form of electronic word-of-mouth for sharing consumer opinions concerning brands. We analyzed more than 150,000 microblog postings containing branding comments, sentiments, and opinions. We investigated the overall structure of these microblog postings, the types of expressions, and the movement in positive or negative sentiment. We compared automated methods of classifying sentiment in these microblogs with manual coding. Using a case study approach, we analyzed the range, frequency, timing, and content of tweets in a corporate account. Our research findings show that 19% of microblogs contain mention of a brand. Of the branding microblogs, nearly 20% contained some expression of brand sentiments. Of these, more than 50% were positive and 33% were critical of the company or product. Our comparison of automated and manual coding showed no significant differences between the two approaches. In analyzing microblogs for structure and composition, the linguistic structure of tweets approximate the linguistic patterns of natural language expressions. We find that microblogging is an online tool for customer word of mouth communications and discuss the implications for corporations using microblogging as part of their overall marketing strategy.

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Abstract

The goal of this research is to evaluate the effect of ad rank on the performance of keyword advertising campaigns. We examined a large-scale data file comprised of nearly 7,000,000 records spanning 33 consecutive months of a major US retailer's search engine marketing campaign. The theoretical foundation is serial position effect to explain searcher behavior when interacting with ranked ad listings. We control for temporal effects and use one-way analysis of variance (ANOVA) with Tamhane's T2 tests to examine the effect of ad rank on critical keyword advertising metrics, including clicks, cost-per-click, sales revenue, orders, items sold, and advertising return on investment. Our findings show significant ad rank effect on most of those metrics, although less effect on conversion rates. A primacy effect was found on both clicks and sales, indicating a general compelling performance of top-ranked ads listed on the first results page. Conversion rates, on the other hand, follow a relatively stable distribution except for the top 2 ads, which had significantly higher conversion rates. However, examining conversion potential (the effect of both clicks and conversion rate), we show that ad rank has a significant effect on the performance of keyword advertising campaigns. Conversion potential is a more accurate measure of the impact of an ad's position. In fact, the first ad position generates about 80% of the total profits, after controlling for advertising costs. In addition to providing theoretical grounding, the research results reported in this paper are beneficial to companies using search engine marketing as they strive to design more effective advertising campaigns.

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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

Many people turn to their social networks to find information through the practice of question and answering. We believe it is necessary to use different answering strategies based on the type of questions to accommodate the different information needs. In this research, we propose the ASK taxonomy that categorizes questions posted on social networking sites into three types according to the nature of the questioner's inquiry of accuracy, social, or knowledge. To automatically decide which answering strategy to use, we develop a predictive model based on ASK question types using question features from the perspectives of lexical, topical, contextual, and syntactic as well as answer features. By applying the classifier on an annotated data set, we present a comprehensive analysis to compare questions in terms of their word usage, topical interests, temporal and spatial restrictions, syntactic structure, and response characteristics. Our research results show that the three types of questions exhibited different characteristics in the way they are asked. Our automatic classification algorithm achieves an 83% correct labeling result, showing the value of the ASK taxonomy for the design of social question and answering systems.

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Abstract

Searchers seldom make use of the advanced searching features that could improve the quality of the search process because they do not know these features exist, do not understand how to use them, or do not believe they are effective or efficient. Information retrieval systems offering automated assistance could greatly improve search effectiveness by suggesting or implementing assistance automatically. A critical issue in designing such systems is determining when the system should intervene in the search process. In this paper, we report the results of an empirical study analyzing when during the search process users seek automated searching assistance from the system and when they implement the assistance. We designed a fully functional, automated assistance application and conducted a study with 30 subjects interacting with the system. The study used a 2G TREC document collection and TREC topics. Approximately 50% of the subjects sought assistance, and over 80% of those implemented that assistance. Results from the evaluation indicate that users are willing to accept automated assistance during the search process, especially after viewing results and locating relevant documents. We discuss implications for interactive information retrieval system design and directions for future research.

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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.