Search (30 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

Traffic from search engines is important for most online businesses, with the majority of visitors to many websites being referred by search engines. Therefore, an understanding of this search engine traffic is critical to the success of these websites. Understanding search engine traffic means understanding the underlying intent of the query terms and the corresponding user behaviors of searchers submitting keywords. In this research, using 712,643 query keywords from a popular Spanish music website relying on contextual advertising as its business model, we use a k-means clustering algorithm to categorize the referral keywords with similar characteristics of onsite customer behavior, including attributes such as clickthrough rate and revenue. We identified 6 clusters of consumer keywords. Clusters range from a large number of users who are low impact to a small number of high impact users. We demonstrate how online businesses can leverage this segmentation clustering approach to provide a more tailored consumer experience. Implications are that businesses can effectively segment customers to develop better business models to increase advertising conversion rates.

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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

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

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

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

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

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

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

In previous articles, we reported the state of Web searching in 1997 (Jansen, Spink, & Saracevic, 2000) and in 1999 (Spink, Wolfram, Jansen, & Saracevic, 2001). Such snapshot studies and statistics on Web use appear regularly (OCLC, 1999), but provide little information about Web searching trends. In this article, we compare and contrast results from our two previous studies of Excite queries' data sets, each containing over 1 million queries submitted by over 200,000 Excite users collected on 16 September 1997 and 20 December 1999. We examine how public Web searching changing during that 2-year time period. As Table 1 shows, the overall structure of Web queries in some areas did not change, while in others we see change from 1997 to 1999. Our comparison shows how Web searching changed incrementally and also dramatically. We see some moves toward greater simplicity, including shorter queries (i.e., fewer terms) and shorter sessions (i.e., fewer queries per user), with little modification (addition or deletion) of terms in subsequent queries. The trend toward shorter queries suggests that Web information content should target specific terms in order to reach Web users. Another trend was to view fewer pages of results per query. Most Excite users examined only one page of results per query, since an Excite results page contains ten ranked Web sites. Were users satisfied with the results and did not need to view more pages? It appears that the public continues to have a low tolerance of wading through retrieved sites. This decline in interactivity levels is a disturbing finding for the future of Web searching. Queries that included Boolean operators were in the minority, but the percentage increased between the two time periods. Most Boolean use involved the AND operator with many mistakes. The use of relevance feedback almost doubled from 1997 to 1999, but overall use was still small. An unusually large number of terms were used with low frequency, such as personal names, spelling errors, non-English words, and Web-specific terms, such as URLs. Web query vocabulary contains more words than found in large English texts in general. The public language of Web queries has its own and unique characteristics. How did Web searching topics change from 1997 to 1999? We classified a random sample of 2,414 queries from 1997 and 2,539 queries from 1999 into 11 categories (Table 2). From 1997 to 1999, Web searching shifted from entertainment, recreation and sex, and pornography, preferences to e-commerce-related topics under commerce, travel, employment, and economy. This shift coincided with changes in information distribution on the publicly indexed Web.

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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

In this paper, we define and present a comprehensive classification of user intent for Web searching. The classification consists of three hierarchical levels of informational, navigational, and transactional intent. After deriving attributes of each, we then developed a software application that automatically classified queries using a Web search engine log of over a million and a half queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the results from this manual classification to the results determined by the automated method. This comparison showed that the automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is vague or multi-faceted, pointing to the need for probabilistic classification. We discuss how search engines can use knowledge of user intent to provide more targeted and relevant results in Web searching.

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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

The performance and capabilities of Web search engines is an important and significant area of research. Millions of people world wide use Web search engines very day. This paper reports the results of a major study examining the overlap among results retrieved by multiple Web search engines for a large set of more than 10,000 queries. Previous smaller studies have discussed a lack of overlap in results returned by Web search engines for the same queries. The goal of the current study was to conduct a large-scale study to measure the overlap of search results on the first result page (both non-sponsored and sponsored) across the four most popular Web search engines, at specific points in time using a large number of queries. The Web search engines included in the study were MSN Search, Google, Yahoo! and Ask Jeeves. Our study then compares these results with the first page results retrieved for the same queries by the metasearch engine Dogpile.com. Two sets of randomly selected user-entered queries, one set was 10,316 queries and the other 12,570 queries, from Infospace's Dogpile.com search engine (the first set was from Dogpile, the second was from across the Infospace Network of search properties were submitted to the four single Web search engines). Findings show that the percent of total results unique to only one of the four Web search engines was 84.9%, shared by two of the three Web search engines was 11.4%, shared by three of the Web search engines was 2.6%, and shared by all four Web search engines was 1.1%. This small degree of overlap shows the significant difference in the way major Web search engines retrieve and rank results in response to given queries. Results point to the value of metasearch engines in Web retrieval to overcome the biases of individual 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 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 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

We report quantitative and qualitative results of an empirical evaluation to determine whether automated assistance improves searching performance and when searchers desire system intervention in the search process. Forty participants interacted with two fully functional information retrieval systems in a counterbalanced, within-participant study. The systems were identical in all respects except that one offered automated assistance and the other did not. The study used a client-side automated assistance application, an approximately 500,000-document Text REtrieval Conference content collection, and six topics. Results indicate that automated assistance can improve searching performance. However, the improvement is less dramatic than one might expect, with an approximately 20% performance increase, as measured by the number of userselected relevant documents. Concerning patterns of interaction, we identified 1,879 occurrences of searchersystem interactions and classified them into 9 major categories and 27 subcategories or states. Results indicate that there are predictable patterns of times when searchers desire and implement searching assistance. The most common three-state pattern is Execute Query-View Results: With Scrolling-View Assistance. Searchers appear receptive to automated assistance; there is a 71% implementation rate. There does not seem to be a correlation between the use of assistance and previous searching performance. We discuss the implications for the design of information retrieval systems and future research directions.