Search results

This study aims to present a new web page recommendation system that can help users to reduce navigational time on the internet.

The proposed design is based on the primacy effect of browsing behavior, that users prefer top ranking items in search results. This approach is intuitive and requires no training data at all.

A user study showed that users are more satisfied with the proposed search methods than with general search engines using hot keywords. Moreover, two performance measures confirmed that the proposed search methods out‐perform other metasearch and search engines.

The research has limitations and future work is planned along several directions. First, the search methods implemented are primarily based on the keyword match between the contents of web pages and the user query items. Using the semantic web to recommend concepts and items relevant to the user query might be very helpful in finding the exact contents that users want, particularly when the users do not have enough knowledge about the domains in which they are searching. Second, offering a mechanism that groups search results to improve the way search results are segmented and displayed also assists users to locate the contents they need. Finally, more user feedback is needed to fine‐tune the search parameters including α and β to improve the performance.

The proposed model can be used to improve the search performance of any search engine.

First, compared with the democratic voting procedure used by metasearch engines, search engine vector voting (SVV) enables a specific combination of search parameters, denoted as α and β, to be applied to a voted search engine, so that users can either narrow or expand their search results to meet their search preferences. Second, unlike page quality analysis, the hyperlink prediction (HLP) determines qualified pages by simply measuring their user behavior function (UBF) values, and thus takes less computing power. Finally, the advantages of HLP over statistical analysis are that it does not need training data, and it can target both multi‐site and site‐specific analysis.

This paper aims to focus on the design of algorithms and techniques for an effective set expansion. A tool that finds and extracts candidate sets of tuples from the World Wide Web was designed and implemented. For instance, when a given user provides <Indonesia, Jakarta, Indonesian Rupiah>, <China, Beijing, Yuan Renminbi>, <Canada, Ottawa, Canadian Dollar> as seeds, our system returns tuples composed of countries with their corresponding capital cities and currency names constructed from content extracted from Web pages retrieved.

The seeds are used to query a search engine and to retrieve relevant Web pages. The seeds are also used to infer wrappers from the retrieved pages. The wrappers, in turn, are used to extract candidates. The Web pages, wrappers, seeds and candidates, as well as their relationships, are vertices and edges of a heterogeneous graph. Several options for ranking candidates from PageRank to truth finding algorithms were evaluated and compared. Remarkably, all vertices are ranked, thus providing an integrated approach to not only answer direct set expansion questions but also find the most relevant pages to expand a given set of seeds.

The experimental results show that leveraging the truth finding algorithm can indeed improve the level of confidence in the extracted candidates and the sources.

Current approaches on set expansion mostly support sets of atomic data expansion. This idea can be extended to the sets of tuples and extract relation instances from the Web given a handful set of tuple seeds. A truth finding algorithm is also incorporated into the approach and it is shown that it can improve the confidence level in the ranking of both candidates and sources in set of tuples expansion.

This study explores the importance of application search engine (ASE) technology in the omni-channel strategy. For this purpose, this chapter firstly explains the concepts of the omni-channel and the search engines and the importance of them. Then, omni-channel in the framework of ASEs is discussed. Finally, recommendations for further researches are presented.

Bringing renewed attention to the anemic representation of Black women within the teaching profession, this chapter begins by chronicling the history of Black women in teacher education – from the Reconstruction Era to the 21st century – in an effort to highlight the causes of their conspicuous demographic decline. Next, it is argued that increasing the number of Black women in the teaching profession is a worthwhile endeavor although the rationales for such targeted efforts may not be obvious or appreciated by the casual observer. It is, therefore, important to illuminate the multiple justifications as to why it is essential to improve the underrepresentation of Black women in America’s classrooms. Lastly, it is asserted that serious attention is required to reverse the dramatic exodus of Black women from the teaching profession. In conveying this issue, the author shares special emphasis recruiting tactics, for the national, programmatic, and local school district levels, as promising proposals to enlist and retain more Black women in the teaching profession.

This paper aims to propose a model entitled MMSPhiD (multidimensional similarity metrics model for screen reader user to phishing detection) that amalgamates multiple approaches to detect phishing URLs.

The model consists of three major components: machine learning-based approach, typosquatting-based approach and phoneme-based approach. The major objectives of the proposed model are detecting phishing URL, typosquatting and phoneme-based domain and suggesting the legitimate domain which is targeted by attackers.

The result of the experiment shows that the MMSPhiD model can successfully detect phishing with 99.03 per cent accuracy. In addition, this paper has analyzed 20 leading domains from Alexa and identified 1,861 registered typosquatting and 543 phoneme-based domains.

The proposed model has used machine learning with the list-based approach. Building and maintaining the list shall be a limitation.

The results of the experiments demonstrate that the model achieved higher performance due to the incorporation of multi-dimensional filters.

In addition, this paper has incorporated the accessibility needs of persons with visual impairments and provides an accessible anti-phishing approach.

This paper assists persons with visual impairments on detection phoneme-based phishing domains.

Considers the problem of improving the performance of Web access by proposing a reconstruction of the internal link structure of a Web site in order to match the quality of the pages (measured in terms of their link importance in the Web space – global ranking) with the popularity of the pages (measured in terms of their importance recognized by Web users – local metrics). Provides a set of simple algorithms for local reorganization of a Web site, which results in improving users’ access to quality pages in an easy and quick way.

Tourism and crime are closely related phenomena, and security is one of the basic preconditions for the functioning of tourism since tourists and tourist areas have many characteristics that make them vulnerable to crime. In this chapter are presented the actual (objective) risk of crime and tourists victimization, visible in statistics on committed crimes and crime victims surveys, and the perceived (subjective) risk of crime, recorded in surveys conducted with tourists. The characteristics which influence the actual and perceived risk of crime and violence are presented by analysing three key elements in the relationship between tourism and crime: (1) tourist (these characteristics are classified as socio-demographic, socio-cultural and psychological); (2) trip (characteristics are the purpose of the trip, travel party, and stage of the trip); and (3) destination (characteristics are crime rates in destination, the occurrence of crime by place and time, type of accommodation and length of stay).