Research on Vertical Search Method of Multidimensional Resources in English Discipline Based on Edge Computing

The traditional vertical search method only considers the content of the webpage, and the global master node is not enough, which will lead to premature convergence and fall into the local optimum, resulting in insufficient multi-dimensional search of resources. Therefore, this paper proposes a multidimensional resource vertical edge based on the calculation of English subject search method. This paper analyzes the architecture of search engine firstly and then introduces the multiaccess edge computing architecture. At last, it constructs the vertical search task computing model of multidimensional resources in English discipline. By associating and traversing the attributes of multidimensional resources of English discipline, the vertical search of attribute information is realized offline, and the vertical search method of multidimensional resources of English discipline based on edge calculation is designed. In order to verify the effectiveness of the proposed method, a comparative experiment is designed. Experimental results show that the method can improve the resource search ratio and recall ratio, and it can also effectively improve the search efficiency. For an English subject resource data of 50 MB, the calculation methods of edge multidimensional resource data search recall rate can reach 97% and multidimensional resource data search time consumption is only 39 ms. The experimental results show that the performance of English subject multidimensional resources vertical search is much better.

Dataset or Not? A Study on the Veracity of Semantic Markup for Dataset Pages

Semantic markup, such as , allows providers on the Web to describe content using a shared controlled vocabulary. This markup is invaluable in enabling a broad range of applications, from vertical search engines, to rich snippets in search results, to actions on emails, to many others. In this paper, we focus on semantic markup for datasets, specifically in the context of developing a vertical search engine for datasets on the Web, Google’s Dataset Search. Dataset Search relies on to identify pages that describe datasets. While was the core enabling technology for this vertical search, we also discovered that we need to address the following problem: pages from 61% of internet hosts that provide markup do not actually describe datasets. We analyze the veracity of dataset markup for Dataset Search’s Web-scale corpus and categorize pages where this markup is not reliable. We then propose a way to drastically increase the quality of the dataset metadata corpus by developing a deep neural-network classifier that identifies whether or not a page with markup is a dataset page. Our classifier achieves 96.7% recall at the 95% precision point. This level of precision enables Dataset Search to circumvent the noise in semantic markup and to use the metadata to provide high quality results to users.

Vertical Result Page Generation for Academic Web Searching

Vertical search engines are meant for answering a user's web query within a specific domain such as news, media, and academic web searching. One main difference between vertical and horizontal web searching is that in vertical web searching, unlike horizontal web searching, a subset of entire web is engaged. The chapter investigates the state-of-the-art in academic web searching and points out shortcomings in this particular domain. Lastly, the authors aimed to propose a summary-based recommender to respond to a user's query by retrieving and ranking them according to their similarity merits on the basis of papers' summaries. Results of the evaluations revealed the fact that the proposed framework has outperformed the state-of-the-art in different metrics such as unanimous ranks and F1 measures.

UCrawler: A learning-based web crawler using a URL knowledge base

Focused crawlers, as fundamental components of vertical search engines, focus on crawling the web pages related to a specific topic. Existing focused crawlers commonly suffer from the problems of low efficiency of crawling pages and subject migration. In this paper, we propose a learning-based focused crawler using a URL knowledge base. To improve the accuracy of similarity, the similarity of the topic is measured with the parent page content, anchor information, and URL content. The URL content is also learned and updated iteratively and continuously. Within the crawler, we implement a crawling mechanism based on a combination of content analysis and simple link analysis crawler strategy, which decreases computational complexity and avoids the locality problem of crawling. Experimental results show that our proposed algorithm achieves a better precision than traditional methods including the shark-search and best-first search algorithms, and avoids the local optimum problem of crawling.

Young consumers’ motivational drivers of brand engagement behavior on social media sites

Purpose A comprehensive operational framework is proposed to explain young consumers’ (i.e. generations Y and Z) engagement with brands on social media sites (SMSs). This paper aims to synthesize two motivational theories: uses and gratifications (U&G) theory and the technology acceptance model (TAM). Design/methodology/approach A selective literature review was conducted to examine recent publications related to young consumers’ brand-driven engagement behavior on SMSs in which either TAM or U&G theory was applied. A three-stage method was used: an initial search was followed by vertical and horizontal searches and then a targeted search of scholarly publications. At each stage, the university’s library databases and Google Scholar were searched for relevant, mainly peer-reviewed articles, using appropriate filters and keywords. The articles’ references and the studies that cited those articles were added to the initially identified research pool (vertical search), coupled with publications of a similar nature based on keywords (horizontal search). The final stage, the targeted search, involved identifying and adding specific articles (e.g. literature reviews and integrated models). Findings After a review of a significant number of U&G and TAM studies, similarities and differences of the two theories were identified, and an integrated operational framework was developed. Based on empirical findings of existing U&G and TAM studies, testable propositions were presented. Research limitations/implications The proposed hybrid model and the associated propositions provide a research opportunity to empirically examine how young consumers’ motivational (i.e. motivating and demotivating) drivers, normative influence, perceived value and attitudes (toward brand content and engagement) predict intention or actual brand-related behavior on SMSs. Practical implications Much of current research indicates that generations Y and Z (“digital natives”) spend considerably more time on SMSs than any of the older generations (“digital immigrants”). Thus, brands that aim to target this cohort need to develop successful engagement strategies (e.g. gamification and influencer marketing) on current and emerging SMSs. The suggested conceptualization provides guidelines for companies to effectively use such communication strategies to motivate young people to engage with their brands on sites such as Twitter, Instagram and Facebook. Originality/value A review of TAM research indicates that it lacks rich motivating/demotivating constructs, and thus borrows from other theories to complement this weakness. An examination of U&G frameworks, particularity Ducoffe (1996)-based models, indicates that these frameworks mainly test engagement with social media advertising but seldom other types of brand-driven engagement on SMSs. In addition, many U&G studies focus less than TAM studies do on outcome variables such as behavioral intentions and behavior. Thus, the authors propose a synthesized U&G and TAM framework that mitigates both theories’ weaknesses and builds on their strengths, enriching the growing research on brand-driven engagement behavior via SMSs.