Search results

As a relatively new computing paradigm, crowdsourcing has gained enormous attention in the recent decade. Its compliance with the Web 2.0 principles, also, puts forward unprecedented opportunities to empower the related services and mechanisms by leveraging humans’ intelligence and problem solving abilities. With respect to the pivotal role of search engines in the Web and information community, this paper aims to investigate the advantages and challenges of incorporating people – as intelligent agents – into search engines’ workflow.

To emphasize the role of the human in computational processes, some specific and related areas are studied. Then, through studying the current trends in the field of crowd-powered search engines and analyzing the actual needs and requirements, the perspectives and challenges are discussed.

As the research on this topic is still in its infancy, it is believed that this study can be considered as a roadmap for future works in the field. In this regard, current status and development trends are delineated through providing a general overview of the literature. Moreover, several recommendations for extending the applicability and efficiency of next generation of crowd-powered search engines are presented. In fact, becoming aware of different aspects and challenges of constructing search engines of this kind can shed light on the way of developing working systems with respect to essential considerations.

The present study was aimed to portrait the big picture of crowd-powered search engines and possible challenges and issues. As one of the early works that provided a comprehensive report on different aspects of the topic, it can be regarded as a reference point.

The purpose of this study is to identify research perspectives/clusters in the field of urban tourism (city tourism) in narrow sense and tourism cities (cities and tourism) in the broader sense to examine the complex relationship through the optics of science mapping. This paper believes that the existing qualitative assessments of this field can be experimentally verified and visualized.

First, the key conceptual dilemmas of research perspectives in urban tourism are highlighted. Based on the Web of Science (WOS) Core Collection and the VOSviewer (computer program for visualizing bibliometric networks), the data will be analyzed. Clustering is used to evaluate information retrieval (inclusivity or selectivity of the search query), publication patterns (journal articles), author keywords, terminology and to identify the respective cities and author collaborations between countries.

Terminological specificities and their contextuality (authors’ preferences) are elaborated, as the topic is studied by authors from different disciplinary fields. Compared to other specific tourisms, urban tourism includes geographic terms (variations of city names) and terms with different connotations (travelers, visitors). Recent Spanish (also Portuguese) linguistic/geographic contexts are noticeable and a strong presence of WOS Emerging Sources Citation Index papers. Research perspectives are represented in the network of clusters of connected terms. If the search is based on a narrower sense of strict urban tourism, then tourism-business topics predominate. If tourism and cities are less closely linked, socio-cultural and environmental-spatial perspectives emerge, as does tourism/cities vulnerability (climate change and health issues).

The construction of a search syntax for the purpose of retrieval is always marked by compromises, given different terminological usages. A narrow search query will miss many relevant documents. On the other hand, if the query is too general, it returns less relevant documents. To this end, this paper tested queries on three different levels of inclusivity or selectivity. More consistent use of terms would benefit authors in the field of urban tourism when searching for references (information retrieval) and, as a consequence, would allow better integration of the field.

This study provides a practical method for evaluating cities and tourism in combining the expertise of an information scientist and a sociologist. It points out numerous caveats in information retrieval. It offers an overview of publishing just prior to the outbreak of Covid-19, thus providing an opportunity for further comparative studies.

This study is the first to examine urban tourism using such a method and can serve as a complement to the existing systematization of qualitative approaches. The findings are consistent with numerous qualitative assessments of weak the research interconnection between the specifics of cities and tourism in terms of broader socio-spatial processes. However, the study suggests that such research linkage is increasing, which is noticeable in relation to issues of social sustainability (e.g. overtourism, Airbnb and touristification).

The purpose of this paper is to study what extent readers’ socio-demographic characteristics, literary preferences and search behavior predict success in fiction search in library catalogs.

In total, 80 readers searched for interesting novels in four differing search tasks. Their search actions were recorded with a Morae Recorder. Pre- and post-questionnaires elicited information about their background, literary preferences and search experience. Readers’ literary preferences were grouped into four orientations by a factor analysis. Linear regression analysis was applied for predicting search success as measured by books’ interest scores.

Most literary orientations contributed to search success, but in differing search tasks. The role of result examination was greater compared to querying in contributing search success almost in each task. The proportion of variance explained in books’ interest scores varied between 5 (open-ended browsing) and 50 percent (analogy search).

The distribution of participants was biased toward females, and the results are aggregated within search session, both reducing the variation of the phenomenon observed.

This study is one of the first to explore how readers’ literary preferences and searching are associated with finding interesting novels, i.e. search success, in library catalogs. The results expand and support the findings in Mikkonen and Vakkari (2017) concerning associations between reader characteristics and fiction search success.

The performance of behavioral targeting (BT) mainly relies on the effectiveness of user classification since advertisers always want to target their advertisements to the most relevant users. In this paper, the authors frame the BT as a user classification problem and describe a machine learning–based approach for solving it.

To perform such a study, two major research questions are investigated: the first question is how to represent a user’s online behavior. A good representation strategy should be able to effectively classify users based on their online activities. The second question is how different representation strategies affect the targeting performance. The authors propose three user behavior representation methods and compare them empirically using the area under the receiver operating characteristic curve (AUC) as a performance measure.

The experimental results indicate that ad campaign effectiveness can be significantly improved by combining user search queries, clicked URLs and clicked ads as a user profile. In addition, the authors also explore the temporal aspect of user behavior history by investigating the effect of history length on targeting performance. The authors note that an improvement of approximately 6.5% in AUC is achieved when user history is extended from 1 day to 14 days, which is substantial in targeting performance.

This paper confirms the effectiveness of BT on user classification and provides a validation of BT for Internet advertising.

The study aims to paint a representative picture of the current state of search interfaces of Swedish online museum collections, focussing on search functionalities with particular reference to subject searching, as well as the use of controlled vocabularies, with the purpose of identifying which improvements of the search interfaces are needed to ensure high-quality information retrieval for the end user.

In the first step, a set of 21 search interface criteria was identified, based on related research and current standards in the domain of cultural heritage knowledge organization. Secondly, a complete set of Swedish museums that provide online access to their collections was identified, comprising nine cross-search services and 91 individual museums' websites. These 100 websites were each evaluated against the 21 criteria, between 1 July and 31 August 2020.

Although many standards and guidelines are in place to ensure quality-controlled subject indexing, which in turn support information retrieval of relevant resources (as individual or full search results), the study shows that they are not broadly implemented, resulting in information retrieval failures for the end user. The study also demonstrates a strong need for the implementation of controlled vocabularies in these museums.

This study is a rare piece of research which examines subject searching in online museums; the 21 search criteria and their use in the analysis of the complete set of online collections of a country represents a considerable and unique contribution to the fields of knowledge organization and information retrieval of cultural heritage. Its particular value lies in showing how the needs of end users, many of which are documented and reflected in international standards and guidelines, should be taken into account in designing search tools for these museums; especially so in subject searching, which is the most complex and yet the most common type of search. Much effort has been invested into digitizing cultural heritage collections, but access to them is hindered by poor search functionality. This study identifies which are the most important aspects to improve.

The author investigates whether investors’ online information demand measured by Google search query and the changes in the numbers of Wikipedia page view can explain and predict stock return, trading volume and volatility dynamics of companies listed on the Nigerian Stock Exchange.

The multiple regression model which encompasses both the univariate and multivariate regression framework was employed as the research methodology. As part of our pre-analysis, we test for multicollinearity and applied the Wu/Hausman specification test to detect whether endogeneity exist in the regression model.

We provide novel and robust evidence that Google searches neither explain the contemporaneous nor predict stock return, trading volume and volatility dynamics. Similarly, results also indicate that trading volume and volatility dynamics have no relationship with changes in the numbers of Wikipedia pages view related to stock activities.

This study opens new strand of empirical literature of “investors' attention” in the context of African stock markets as empirical evidence. No evidence from previous studies on investors' attention exist, whether in Google search query or Wikipedia page view, with respect to African stock markets, particularly the Nigerian stock market. This study seeks to bridge these knowledge gaps by examining these relations.

Universities play an important role in the promotion and implementation of the 2030 Agenda for Sustainable Development. This study aims to examine the visibility of information about the Sustainable Development Goals (SDGs) on the websites of Spanish and major international universities, by means of a quantitative and qualitative analysis with an online visibility management platform that makes use of big data technology.

The Web visibility of the universities studied in relation to the terms “SDG”, “Sustainable Development Goals” and “2030 Agenda” was determined using the SEMrush tool. Information was obtained on the number of web pages accessed and the queries formulated (query expansion). The content indexed by Google for these universities was compiled, and finally, the search engine optimization (SEO) factors applicable to the websites with the highest Web visibility were identified.

The universities analysed are content creators but do not have very high Web visibility in Web searches for information on the SDGs. Of the 98 universities analysed, only four feature prominently in search results.

Although research exists on the application of SEO to different areas, there have not, to date, been any studies examining the Web visibility of universities in relation to Web searches for information on the 2030 Agenda. The main contributions of this study are the global perspective it provides on the Web visibility of content produced by universities about the SDGs and the recommendations it offers for improving that visibility.

Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.

In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.

The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.

To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.