User-web interactions : how wholistic/analytic web users search the web?

User-Web interactions have emerged as an important research in the field of information science. In this study, we examine extensively the Web searching performed by general users. Our goal is to investigate the effects of users’ cognitive styles on their Web search behavior in relation to two broad components: Information Searching and Information Processing Approaches. We use questionnaires, a measure of cognitive style, Web session logs and think-aloud as the data collection instruments. Our study findings show wholistic Web users tend to adopt a top-down approach to Web searching, where the users searched for a generic topic, and then reformulate their queries to search for specific information. They tend to prefer reading to process information. Analytic users tend to prefer a bottom-up approach to information searching and they process information by scanning search result pages.

New media services : ratings and recommendations websites in the travel and tourism industry

The travel and hospitality industry is one which relies especially crucially on word of mouth, both at the level of overall destinations (Australia, Queensland, Brisbane) and at the level of travellers’ individual choices of hotels, restaurants, sights during their trips. The provision of such word-of-mouth information has been revolutionised over the past decade by the rise of community-based Websites which allow their users to share information about their past and future trips and advise one another on what to do or what to avoid during their travels. Indeed, the impact of such user-generated reviews, ratings, and recommendations sites has been such that established commercial travel advisory publishers such as Lonely Planet have experienced a pronounced downturn in sales ¬– unless they have managed to develop their own ways of incorporating user feedback and contributions into their publications. This report examines the overall significance of ratings and recommendation sites to the travel industry, and explores the community, structural, and business models of a selection of relevant ratings and recommendations sites. We identify a range of approaches which are appropriate to the respective target markets and business aims of these organisations, and conclude that there remain significant opportunities for further operators especially if they aim to cater for communities which are not yet appropriately served by specific existing sites. Additionally, we also point to the increasing importance of connecting stand-alone ratings and recommendations sites with general social media spaces like Facebook, Twitter, and LinkedIn, and of providing mobile interfaces which enable users to provide updates and ratings directly from the locations they happen to be visiting. In this report, we profile the following sites: * TripAdvisor, the international market leader for travel ratings and recommendations sites, with a membership of some 11 million users; * IgoUgo, the other leading site in this field, which aims to distinguish itself from the market leader by emphasising the quality of its content; * Zagat, a long-established publisher of restaurant guides which has translated its crowdsourcing model from the offline to the online world; * Lonely Planet’s Thorn Tree site, which attempts to respond to the rise of these travel communities by similarly harnessing user-generated content; * Stayz, which attempts to enhance its accommodation search and booking services by incorporating ratings and reviews functionality; and * BigVillage, an Australian-based site attempting to cater for a particularly discerning niche of travellers; * Dopplr, which connects travel and social networking in a bid to pursue the lucrative market of frequent and business travellers; * Foursquare, which builds on its mobile application to generate a steady stream of ‘check-ins’ and recommendations for hospitality and other services around the world; * Suite 101, which uses a revenue-sharing model to encourage freelance writers to contribute travel writing (amongst other genres of writing); * Yelp, the global leader in general user-generated product review and recommendation services. In combination, these profiles provide an overview of current developments in the travel ratings and recommendations space (and beyond), and offer an outlook for further possibilities. While no doubt affected by the global financial downturn and the reduction in travel that it has caused, travel ratings and recommendations remain important – perhaps even more so if a reduction in disposable income has resulted in consumers becoming more critical and discerning. The aggregated word of mouth from many tens of thousands of travellers which these sites provide certainly has a substantial influence on their users. Using these sites to research travel options has now become an activity which has spread well beyond the digirati. The same is true also for many other consumer industries, especially where there is a significant variety of different products available – and so, this report may also be read as a case study whose findings are able to be translated, mutatis mutandis, to purchasing decisions from household goods through consumer electronics to automobiles.

Modelling web search behaviour : a user’s cognitive style perspective [Unpublished]

Many researchers have investigated and modelled aspects of Web searching. A number of studies have explored the relationships between individual differences and Web searching. However, limited studies have explored the role of users’ cognitive styles in determining Web searching behaviour. Current models of Web searching have limited consideration of users’ cognitive styles. The impact of users’ cognitive style on Web searching and their relationships are little understood or represented. Individuals differ in their information processing approaches and the way they represent information, thus affecting their performance. To create better models of Web searching we need to understand more about user’s cognitive style and their Web search behaviour, and the relationship between them. More rigorous research is needed in using more complex and meaningful measures of relevance; across a range of different types of search tasks and different populations of Internet users. The project further explores the relationships between the users’ cognitive style and their Web searching. The project will develop a model depicting the relationships between a user’s cognitive style and their Web searching. The related literature, aims and objectives and research design are discussed.

The international search for ethics norms : which consumer behaviors do consumers consider (un)acceptable?

Purpose: Businesses cannot rely on their customers to always do the right thing. To help researchers and service providers better understand the dark (and light) side of customer behavior, this study aims to aggregate and investigate perceptions of consumer ethics from young consumers on five continents. The study seeks to present a profile of consumer behavioral norms, how ethical inclinations have evolved over time, and country differences. ---------- Design/methodology/approach: Data were collected from ten countries across five continents between 1997 and 2007. A self-administered questionnaire containing 14 consumer scenarios asked respondents to rate acceptability of questionable consumer actions. ---------- Findings: Overall, consumers found four of the 14 questionable consumer actions acceptable. Illegal activities were mostly viewed as unethical, while some legal actions that were against company policy were viewed less harshly. Differences across continents emerged, with Europeans being the least critical, while Asians and Africans shared duties as most critical of consumer actions. Over time, consumers have become less tolerant of questionable behaviors. ---------- Practical implications: Service providers should use the findings of this study to better understand the service customer. Knowing what customers in general believe is ethical or unethical can help service designers focus on the aspects of the technology or design most vulnerable to customer deviance. ---------- Multinationals already know they must adapt their business practices to the market in which they are operating, but they must also adapt their expectations as to the behavior of the corresponding consumer base. Originality/value: This investigation into consumer ethics helps businesses understand what their customer base believes is the right thing in their role as customer. This is a large-scale study of consumer ethics including 3,739 respondents on five continents offering an evolving view of the ethical inclinations of young consumers.

Improving the performance of model-order selection criteria by partial-model selection search

The traditional searching method for model-order selection in linear regression is a nested full-parameters-set searching procedure over the desired orders, which we call full-model order selection. On the other hand, a method for model-selection searches for the best sub-model within each order. In this paper, we propose using the model-selection searching method for model-order selection, which we call partial-model order selection. We show by simulations that the proposed searching method gives better accuracies than the traditional one, especially for low signal-to-noise ratios over a wide range of model-order selection criteria (both information theoretic based and bootstrap-based). Also, we show that for some models the performance of the bootstrap-based criterion improves significantly by using the proposed partial-model selection searching method. Index Terms— Model order estimation, model selection, information theoretic criteria, bootstrap 1. INTRODUCTION Several model-order selection criteria can be applied to find the optimal order. Some of the more commonly used information theoretic-based procedures include Akaike’s information criterion (AIC) [1], corrected Akaike (AICc) [2], minimum description length (MDL) [3], normalized maximum likelihood (NML) [4], Hannan-Quinn criterion (HQC) [5], conditional model-order estimation (CME) [6], and the efficient detection criterion (EDC) [7]. From a practical point of view, it is difficult to decide which model order selection criterion to use. Many of them perform reasonably well when the signal-to-noise ratio (SNR) is high. The discrepancies in their performance, however, become more evident when the SNR is low. In those situations, the performance of the given technique is not only determined by the model structure (say a polynomial trend versus a Fourier series) but, more importantly, by the relative values of the parameters within the model. This makes the comparison between the model-order selection algorithms difficult as within the same model with a given order one could find an example for which one of the methods performs favourably well or fails [6, 8]. Our aim is to improve the performance of the model order selection criteria in cases where the SNR is low by considering a model-selection searching procedure that takes into account not only the full-model order search but also a partial model order search within the given model order. Understandably, the improvement in the performance of the model order estimation is at the expense of additional computational complexity.

Bayesian methodology for genetics of complex diseases

Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.

Critical gap estimation by numerical and statistical highest likelihood search

Many traffic situations require drivers to cross or merge into a stream having higher priority. Gap acceptance theory enables us to model such processes to analyse traffic operation. This discussion demonstrated that numerical search fine tuned by statistical analysis can be used to determine the most likely critical gap for a sample of drivers, based on their largest rejected gap and accepted gap. This method shares some common features with the Maximum Likelihood Estimation technique (Troutbeck 1992) but lends itself well to contemporary analysis tools such as spreadsheet and is particularly analytically transparent. This method is considered not to bias estimation of critical gap due to very small rejected gaps or very large rejected gaps. However, it requires a sufficiently large sample that there is reasonable representation of largest rejected gap/accepted gap pairs within a fairly narrow highest likelihood search band.

Identification of area-level influences on regions of high cancer incidence in Queensland, Australia : a classification tree approach

Background: Strategies for cancer reduction and management are targeted at both individual and area levels. Area-level strategies require careful understanding of geographic differences in cancer incidence, in particular the association with factors such as socioeconomic status, ethnicity and accessibility. This study aimed to identify the complex interplay of area-level factors associated with high area-specific incidence of Australian priority cancers using a classification and regression tree (CART) approach. Methods: Area-specific smoothed standardised incidence ratios were estimated for priority-area cancers across 478 statistical local areas in Queensland, Australia (1998-2007, n=186,075). For those cancers with significant spatial variation, CART models were used to identify whether area-level accessibility, socioeconomic status and ethnicity were associated with high area-specific incidence. Results: The accessibility of a person’s residence had the most consistent association with the risk of cancer diagnosis across the specific cancers. Many cancers were likely to have high incidence in more urban areas, although male lung cancer and cervical cancer tended to have high incidence in more remote areas. The impact of socioeconomic status and ethnicity on these associations differed by type of cancer. Conclusions: These results highlight the complex interactions between accessibility, socioeconomic status and ethnicity in determining cancer incidence risk.

BioPatML.NET – a bioinformatic pattern search engine

In the present paper, we introduce BioPatML.NET, an application library for the Microsoft Windows .NET framework [2] that implements the BioPatML pattern definition language and sequence search engine. BioPatML.NET is integrated with the Microsoft Biology Foundation (MBF) application library [3], unifying the parsers and annotation services supported or emerging through MBF with the language, search framework and pattern repository of BioPatML. End users who wish to exploit the BioPatML.NET engine and repository without engaging the services of a programmer may do so via the freely accessible web-based BioPatML Editor, which we describe below.

Improving web database search incorporating users query information

The growing importance and need of data processing for information extraction is vital for Web databases. Due to the sheer size and volume of databases, retrieval of relevant information as needed by users has become a cumbersome process. Information seekers are faced by information overloading - too many result sets are returned for their queries. Moreover, too few or no results are returned if a specific query is asked. This paper proposes a ranking algorithm that gives higher preference to a user’s current search and also utilizes profile information in order to obtain the relevant results for a user’s query.