DSEORA: Integration of Deep Learning and Metaheuristics for Web Page Recommendation Based on Search Engine Optimization Ranking

2021 ◽  
pp. 873-883
Author(s):  
Ketan Vaish ◽  
Gerard Deepak ◽  
A. Santhanavijayan
Keyword(s):  
Deep Learning ◽  
Search Engine ◽  
Web Page ◽  
Search Engine Optimization

Automatic Keyword Extraction Algorithm and Implementation

2010 ◽  
Vol 44-47 ◽  
pp. 4041-4049 ◽  
Author(s):  
Hong Zhao ◽  
Chen Sheng Bai ◽  
Song Zhu
Keyword(s):  
Search Engine ◽  
Word Frequency ◽  
Search Engines ◽  
Keyword Extraction ◽  
Web Page ◽  
C Language ◽  
Search Engine Optimization ◽  
Page Ranking ◽  
Extraction Algorithm ◽  
A Site

Search engines can bring a lot of benefit to the website. For a site, each page’s search engine ranking is very important. To make web page ranking in search engine ahead, Search engine optimization (SEO) make effect on the ranking. Web page needs to set the keywords as “keywords" to use SEO. The paper focuses on the content of a given word, and extracts the keywords of each page by calculating the word frequency. The algorithm is implemented by C # language. Keywords setting of webpage are of great importance on the information and products

Eccentric Methodology with Optimization to Unearth Hidden Facts of Search Engine Result Pages

2019 ◽  
Vol 12 (2) ◽  
pp. 110-119 ◽  
Author(s):  
Jayaraman Sethuraman ◽  
Jafar A. Alzubi ◽  
Ramachandran Manikandan ◽  
Mehdi Gheisari ◽  
Ambeshwar Kumar
Keyword(s):  
Search Engine ◽  
World Wide ◽  
Optimization Techniques ◽  
Web Pages ◽  
Web Page ◽  
Search Engine Optimization ◽  
The World ◽  
Search Engine Result ◽  
The Web ◽  
New Framework

Background: The World Wide Web houses an abundance of information that is used every day by billions of users across the world to find relevant data. Website owners employ webmasters to ensure their pages are ranked top in search engine result pages. However, understanding how the search engine ranks a website, which comprises numerous web pages, as the top ten or twenty websites is a major challenge. Although systems have been developed to understand the ranking process, a specialized tool based approach has not been tried. Objective: This paper develops a new framework and system that process website contents to determine search engine optimization factors. Methods: To analyze the web page dynamically by assessing the web site content based on specific keywords, elimination method was used in an attempt to reveal various search engine optimization techniques. Conclusion: Our results lead to conclude that the developed system is able to perform a deeper analysis and find factors which play a role in bringing the site on the top of the list.

scholarly journals Using Machine Learning for Web Page Classification in Search Engine Optimization

2021 ◽  
Vol 13 (1) ◽  
pp. 9
Author(s):  
Goran Matošević ◽  
Jasminka Dobša ◽  
Dunja Mladenić
Keyword(s):  
Machine Learning ◽  
Search Engine ◽  
Search Engines ◽  
Machine Learning Algorithms ◽  
Practical Significance ◽  
Web Pages ◽  
Web Page ◽  
Data Set ◽  
Search Engine Optimization ◽  
Novel Approach

This paper presents a novel approach of using machine learning algorithms based on experts’ knowledge to classify web pages into three predefined classes according to the degree of content adjustment to the search engine optimization (SEO) recommendations. In this study, classifiers were built and trained to classify an unknown sample (web page) into one of the three predefined classes and to identify important factors that affect the degree of page adjustment. The data in the training set are manually labeled by domain experts. The experimental results show that machine learning can be used for predicting the degree of adjustment of web pages to the SEO recommendations—classifier accuracy ranges from 54.59% to 69.67%, which is higher than the baseline accuracy of classification of samples in the majority class (48.83%). Practical significance of the proposed approach is in providing the core for building software agents and expert systems to automatically detect web pages, or parts of web pages, that need improvement to comply with the SEO guidelines and, therefore, potentially gain higher rankings by search engines. Also, the results of this study contribute to the field of detecting optimal values of ranking factors that search engines use to rank web pages. Experiments in this paper suggest that important factors to be taken into consideration when preparing a web page are page title, meta description, H1 tag (heading), and body text—which is aligned with the findings of previous research. Another result of this research is a new data set of manually labeled web pages that can be used in further research.

scholarly journals An Offline SEO (Search Engine Optimization) Based Algorithm to Calculate Web Page Rank According to Different Parameters

2013 ◽  
Vol 9 (1) ◽  
pp. 926-931 ◽  
Author(s):  
Parveen Rani ◽  
Er. Sukhpreet Singh
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Web Pages ◽  
Web Page ◽  
Page Rank ◽  
Search Engine Optimization ◽  
Hits Algorithm

SEO stands for Search Engine Optimization. It is a technique that searches various web pages for specified keywords and ranks these Web pages according to some parameters. They are used to feed pages to search engines.  The main importance of SEO is that it helps to find the relevant data and increase the rank of a webpage in search engines’ results. In our paper, we develop a new algorithm M-HITS (Modified HITS) to provide the page rank. M-HITS Algorithm is a new version of HITS algorithm. It is developed by extending the properties of HITS algorithm.

scholarly journals Content Management Systems Performance and Compliance Assessment Based on a Data-Driven Search Engine Optimization Methodology

Information ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 259
Author(s):  
Ioannis Drivas ◽  
Dimitrios Kouis ◽  
Daphne Kyriaki-Manessi ◽  
Georgios Giannakopoulos
Keyword(s):  
Search Engine ◽  
Common Knowledge ◽  
Organizational Structures ◽  
Content Management ◽  
Data Driven ◽  
Management Systems ◽  
Search Engine Optimization ◽  
Optimization Methodology ◽  
Content Management Systems ◽  
The Web

While digitalization of cultural organizations is in full swing and growth, it is common knowledge that websites can be used as a beacon to expand the awareness and consideration of their services on the Web. Nevertheless, recent research results indicate the managerial difficulties in deploying strategies for expanding the discoverability, visibility, and accessibility of these websites. In this paper, a three-stage data-driven Search Engine Optimization schema is proposed to assess the performance of Libraries, Archives, and Museums websites (LAMs), thus helping administrators expand their discoverability, visibility, and accessibility within the Web realm. To do so, the authors examine the performance of 341 related websites from all over the world based on three different factors, Content Curation, Speed, and Security. In the first stage, a statistically reliable and consistent assessment schema for evaluating the SEO performance of LAMs websites through the integration of more than 30 variables is presented. Subsequently, the second stage involves a descriptive data summarization for initial performance estimations of the examined websites in each factor is taking place. In the third stage, predictive regression models are developed to understand and compare the SEO performance of three different Content Management Systems, namely the Drupal, WordPress, and custom approaches, that LAMs websites have adopted. The results of this study constitute a solid stepping-stone both for practitioners and researchers to adopt and improve such methods that focus on end-users and boost organizational structures and culture that relied on data-driven approaches for expanding the visibility of LAMs services.

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

Sign in / Sign up

Export Citation Format

Share Document