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Search Engine Optimization: a Survey of Current Best Practices
Grand Valley State University ScholarWorks@GVSU Technical Library School of Computing and Information Systems 2013 Search Engine Optimization: A Survey of Current Best Practices Niko Solihin Grand Valley Follow this and additional works at: https://scholarworks.gvsu.edu/cistechlib ScholarWorks Citation Solihin, Niko, "Search Engine Optimization: A Survey of Current Best Practices" (2013). Technical Library. 151. https://scholarworks.gvsu.edu/cistechlib/151 This Project is brought to you for free and open access by the School of Computing and Information Systems at ScholarWorks@GVSU. It has been accepted for inclusion in Technical Library by an authorized administrator of ScholarWorks@GVSU. For more information, please contact [email protected]. Search Engine Optimization: A Survey of Current Best Practices By Niko Solihin A project submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Information Systems at Grand Valley State University April, 2013 _______________________________________________________________________________ Your Professor Date Search Engine Optimization: A Survey of Current Best Practices Niko Solihin Grand Valley State University Grand Rapids, MI, USA [email protected] ABSTRACT 2. Build and maintain an index of sites’ keywords and With the rapid growth of information on the web, search links (indexing) engines have become the starting point of most web-related 3. Present search results based on reputation and rele- tasks. In order to reach more viewers, a website must im- vance to users’ keyword combinations (searching) prove its organic ranking in search engines. This paper intro- duces the concept of search engine optimization (SEO) and The primary goal is to e↵ectively present high-quality, pre- provides an architectural overview of the predominant search cise search results while efficiently handling a potentially engine, Google. -
Brand Values and the Bottom Line 1 1
Brand Values and the Bottom Line 1 1. Why You Should Read This Guide 3 2. Common Obstacles to Avoid 4 3. Website Structure 7 4. Keyword Research 8 5. Meta Information 9 Contents 6. Body Content 11 7. Internal Site Linking 12 8. URL Equity 13 9. The Elements of URL Equity 14 10. Assessing URL Equity 15 11. The Consequences of Redesigning Without a URL Strategy 16 12. Migrating URL Equity 17 13. Summary 18 14. Appendix 1 19 15. Appendix 2 20 16. About Investis Digital 21 Brand Values and the Bottom Line 2 1. Why You Should Read This Guide Best Practices: SEO for Website Redesign & Migration outlines organic search optimization best practices for a website redesign, as well as factors to consider in order to maintain the URL equity during a domain or platform migration. This guide illustrates the common pitfalls that you can avoid in the redesign phase of a website, making it possible for a site to gain better visibility within search engines results. Additionally, Best Practices: SEO for Website Redesign & Migration explains the importance of setting up all aspects of a website, To do a deep dive including: directory structure, file names, page content and internal into SEO for website linking. Finally, we illustrate case study examples of successful site redesign and redesigns. migration, contact Reading this guide will set you up Investis Digital. for SEO success when you undergo a website redesign. The guide will We’re here to help. help you avoid costly errors and gain more traffic, leading to valuable conversions. -
Received Citations As a Main SEO Factor of Google Scholar Results Ranking
RECEIVED CITATIONS AS A MAIN SEO FACTOR OF GOOGLE SCHOLAR RESULTS RANKING Las citas recibidas como principal factor de posicionamiento SEO en la ordenación de resultados de Google Scholar Cristòfol Rovira, Frederic Guerrero-Solé and Lluís Codina Nota: Este artículo se puede leer en español en: http://www.elprofesionaldelainformacion.com/contenidos/2018/may/09_esp.pdf Cristòfol Rovira, associate professor at Pompeu Fabra University (UPF), teaches in the Depart- ments of Journalism and Advertising. He is director of the master’s degree in Digital Documenta- tion (UPF) and the master’s degree in Search Engines (UPF). He has a degree in Educational Scien- ces, as well as in Library and Information Science. He is an engineer in Computer Science and has a master’s degree in Free Software. He is conducting research in web positioning (SEO), usability, search engine marketing and conceptual maps with eyetracking techniques. https://orcid.org/0000-0002-6463-3216 [email protected] Frederic Guerrero-Solé has a bachelor’s in Physics from the University of Barcelona (UB) and a PhD in Public Communication obtained at Universitat Pompeu Fabra (UPF). He has been teaching at the Faculty of Communication at the UPF since 2008, where he is a lecturer in Sociology of Communi- cation. He is a member of the research group Audiovisual Communication Research Unit (Unica). https://orcid.org/0000-0001-8145-8707 [email protected] Lluís Codina is an associate professor in the Department of Communication at the School of Com- munication, Universitat Pompeu Fabra (UPF), Barcelona, Spain, where he has taught information science courses in the areas of Journalism and Media Studies for more than 25 years. -
Machine Learning for Marketers
Machine Learning for Marketers A COMPREHENSIVE GUIDE TO MACHINE LEARNING CONTENTS pg 3 Introduction pg 4 CH 1 The Basics of Machine Learning pg 9 CH. 2 Supervised vs Unsupervised Learning and Other Essential Jargon pg 13 CH. 3 What Marketers can Accomplish with Machine Learning pg 18 CH. 4 Successful Machine Learning Use Cases pg 26 CH. 5 How Machine Learning Guides SEO pg 30 CH. 6 Chatbots: The Machine Learning you are Already Interacting with pg 36 CH. 7 How to Set Up a Chatbot pg 45 CH. 8 How Marketers Can Get Started with Machine Learning pg 58 CH. 9 Most Effective Machine Learning Models pg 65 CH. 10 How to Deploy Models Online pg 72 CH. 11 How Data Scientists Take Modeling to the Next Level pg 79 CH. 12 Common Problems with Machine Learning pg 84 CH. 13 Machine Learning Quick Start INTRODUCTION Machine learning is a term thrown around in technol- ogy circles with an ever-increasing intensity. Major technology companies have attached themselves to this buzzword to receive capital investments, and every major technology company is pushing its even shinier parentartificial intelligence (AI). The reality is that Machine Learning as a concept is as days that only lives and breathes data science? We cre- old as computing itself. As early as 1950, Alan Turing was ated this guide for the marketers among us whom we asking the question, “Can computers think?” In 1969, know and love by giving them simpler tools that don’t Arthur Samuel helped define machine learning specifi- require coding for machine learning. -
Package 'Rahrefs'
Package ‘RAhrefs’ July 28, 2019 Type Package Title 'Ahrefs' API R Interface Version 0.1.4 Description Enables downloading detailed reports from <https://ahrefs.com> about backlinks from pointing to website, provides authentication with an API key as well as ordering, grouping and filtering functionalities. License MIT + file LICENCE URL https://ahrefs.com/ BugReports https://github.com/Leszek-Sieminski/RAhrefs/issues Depends R (>= 3.4.0) Imports assertthat, httr, jsonlite, testthat NeedsCompilation no Encoding UTF-8 LazyData true RoxygenNote 6.1.1 Author Leszek Sieminski´ [aut, cre], Performance Media Polska sp. z o.o. [cph, fnd] Maintainer Leszek Sieminski´ <[email protected]> Repository CRAN Date/Publication 2019-07-28 08:40:02 UTC R topics documented: ahrefs_metrics . 2 ahrefs_reports . 2 rah_ahrefs_rank . 3 rah_anchors . 5 rah_anchors_refdomains . 8 rah_auth . 11 rah_backlinks . 11 1 2 ahrefs_metrics rah_backlinks_new_lost . 14 rah_backlinks_new_lost_counters . 17 rah_backlinks_one_per_domain . 20 rah_broken_backlinks . 23 rah_broken_links . 26 rah_condition . 29 rah_condition_set . 31 rah_domain_rating . 32 rah_downloader . 34 rah_linked_anchors . 36 rah_linked_domains . 39 rah_linked_domains_by_type . 42 rah_metrics . 45 rah_metrics_extended . 47 rah_pages . 50 rah_pages_extended . 52 rah_pages_info . 55 rah_refdomains . 58 rah_refdomains_by_type . 61 rah_refdomains_new_lost . 64 rah_refdomains_new_lost_counters . 66 rah_refips . 68 rah_subscription_info . 71 ahrefs_metrics Ahrefs metrics Description Description of Ahrefs -
Webpage Ranking Algorithms Second Exam Report
Webpage Ranking Algorithms Second Exam Report Grace Zhao Department of Computer Science Graduate Center, CUNY Exam Committee Professor Xiaowen Zhang, Mentor, College of Staten Island Professor Ted Brown, Queens College Professor Xiangdong Li, New York City College of Technology Initial version: March 8, 2015 Revision: May 1, 2015 1 Abstract The traditional link analysis algorithms exploit the context in- formation inherent in the hyperlink structure of the Web, with the premise being that a link from page A to page B denotes an endorse- ment of the quality of B. The exemplary PageRank algorithm weighs backlinks with a random surfer model; Kleinberg's HITS algorithm promotes the use of hubs and authorities over a base set; Lempel and Moran traverse this structure through their bipartite stochastic algo- rithm; Li examines the structure from head to tail, counting ballots over hypertext. Semantic Web and its inspired technologies bring new core factors into the ranking equation. While making continuous effort to improve the importance and relevancy of search results, Semantic ranking algorithms strive to improve the quality of search results: (1) The meaning of the search query; and (2) The relevancy of the result in relation to user's intention. The survey focuses on an overview of eight selected search ranking algorithms. 2 Contents 1 Introduction 4 2 Background 5 2.1 Core Concepts . 5 2.1.1 Search Engine . 5 2.1.2 Hyperlink Structure . 5 2.1.3 Search Query . 7 2.1.4 Web Graph . 7 2.1.5 Base Set of Webpages . 9 2.1.6 Semantic Web . -
The Machine Learning Journey with Google
The Machine Learning Journey with Google Google Cloud Professional Services The information, scoping, and pricing data in this presentation is for evaluation/discussion purposes only and is non-binding. For reference purposes, Google's standard terms and conditions for professional services are located at: https://enterprise.google.com/terms/professional-services.html. 1 What is machine learning? 2 Why all the attention now? Topics How Google can support you inyour 3 journey to ML 4 Where to from here? © 2019 Google LLC. All rights reserved. What is machine0 learning? 1 Machine learning is... a branch of artificial intelligence a way to solve problems without explicitly codifying the solution a way to build systems that improve themselves over time © 2019 Google LLC. All rights reserved. Key trends in artificial intelligence and machine learning #1 #2 #3 #4 Democratization AI and ML will be core Specialized hardware Automation of ML of AI and ML competencies of for deep learning (e.g., MIT’s Data enterprises (CPUs → GPUs → TPUs) Science Machine & Google’s AutoML) #5 #6 #7 Commoditization of Cloud as the platform ML set to transform deep learning for AI and ML banking and (e.g., TensorFlow) financial services © 2019 Google LLC. All rights reserved. Use of machine learning is rapidly accelerating Used across products © 2019 Google LLC. All rights reserved. Google Translate © 2019 Google LLC. All rights reserved. Why all the attention0 now? 2 Machine learning allows us to solve problems without codifying the solution. © 2019 Google LLC. All rights reserved. San Francisco New York © 2019 Google LLC. All rights reserved. -
100+ Free SEO Tools & Resources
100+ Free SEO Tools & Resources Keyword Tools ● Keywords Everywhere Great keyword tool Chrome and Firefox extension. (now paid) ● Answer The Public Aggregated view of the questions asked to Google & Bing. ● Keyword Keg Another free keyword tool, you can import lists and export 20 results. ● Wordtracker Scout Displays what articles are about on keyword cloud. ● LSI Graph Generate a list of semantically related keywords and best performing content. ● Google Trends Compare one keyword to another over time ● Keyword Finder Uses Google's autocomplete API to find many keywords. ● KeywordTool.io An alternative to Google keyword planner. ● Merge Words Takes words from 3 lists and merges them together into one. ● Cognitive SEO Keywords Analyze keywords and get suggestions. ● Seed Keywords Grow your own seo keywords from suggestions from your friends. ● Keyword Density Checker Remember to write for people, not SEO, don’t overoptimize. Keyword Rankings ● Small SEO Tools Rank Checker Check rankings of up to 10 keywords at a time. ● Hoth Rankings Tracker Check 10 keywords ranking daily. ● Can I Rank Explains the opportunities and benefits of ranking for certain keywords Backlink Tools ● Ahrefs Backlink Checker Free version of Ahrefs' backlink checker tool. ● Open Link Profiler Fast backlinks and a lot more info worth investigating. ● Check My Links Chrome plugin to check pages for broken links. ● Search Queries for backlink prospecting ● Guide to getting backlinks Lots of great ideas for obtaining a good link or two. ● Help A Reporter Be a reference for reporters looking for answers. Image Optimization ● GeoImgr Image geotagging, add GPS info to photos online. ● EXIF Editor A program for download that allows you to edit EXIF data of images. -
Backlink Building Guide Version 1.0 / December 2019
Backlink Building Guide Version 1.0 / December 2019 Backlink Building Guide Why Backlinks are important Backlinks (links) is and has historically been one of the most important signals for Google to determine if a website or a page content is of importance, valuable, credible, and useful. If Google deems a specific piece of content to have higher importance, be more valuable, more credible and more useful, it will rank higher in their Search Engine Result Page (SERP). One of the things Google uses to determine the positions in SERP is something called page rank, and it’s a direct correlation between the number of backlinks and page rank. Therefore, the more backlinks a website has, the higher the likelihood that the site will also rank higher on Google. • Backlinks are one of the most importance factors of ranking in SERP • Backlinks result in higher probability of ranking higher in SERP Backlink Building Guide Version 1.0 / December 2019 The different types of Backlinks Currently there are two different types of backlinks, follow (also called dofollow) and nofollow. A follow backlink means that Google's algorithms follow the links and the page rank on the receiving website increases. A nofollow backlink is still a link and users can still follow it from one page to another, but Google does not consider nofollow backlinks in its algorithms i.e. it does not produce any effect to rank higher in SERP. The owner of the website decides if it’s a “follow” or “no follow” backlink. Common nofollow backlinks are links that website owners have no control over and are produced by others, for example, comment on blogs, forum posts or sponsored content. -
Profiles in Innovation: Artificial Intelligence
EQUITY RESEARCH | November 14, 2016 Artificial intelligence is the apex technology of the information era. In the latest in our Profiles in Innovation Heath P. Terry, CFA series, we examine how (212) 357-1849 advances in machine [email protected] learning and deep learning Goldman, Sachs & Co. have combined with more Jesse Hulsing powerful computing and an (415) 249-7464 ever-expanding pool of data [email protected] to bring AI within reach for Goldman, Sachs & Co. companies across Mark Grant industries. The development (212) 357-4475 [email protected] of AI-as-a-service has the Goldman, Sachs & Co. potential to open new markets and disrupt the Daniel Powell (917) 343-4120 playing field in cloud [email protected] computing. We believe the Goldman, Sachs & Co. ability to leverage AI will Piyush Mubayi become a defining attribute (852) 2978-1677 of competitive advantage [email protected] for companies in coming Goldman Sachs (Asia) L.L.C. years and will usher in a Waqar Syed resurgence in productivity. (212) 357-1804 [email protected] Goldman, Sachs & Co. PROFILESIN INNOVATION Artificial Intelligence AI, Machine Learning and Data Fuel the Future of Productivity Goldman Sachs does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. Investors should consider this report as only a single factor in making their investment decision. For Reg AC certification and other important disclosures, see the Disclosure Appendix, or go to www.gs.com/research/hedge.html. -
Large-Scale Deep Learning with Tensorflow
Large-Scale Deep Learning With TensorFlow Jeff Dean Google Brain team g.co/brain In collaboration with many other people at Google What is the Google Brain Team? ● Research team focused on long term artificial intelligence research ○ Mix of computer systems and machine learning research expertise ○ Pure ML research, and research in context of emerging ML application areas: ■ robotics, language understanding, healthcare, ... g.co/brain We Disseminate Our Work in Many Ways ● By publishing our work ○ See papers at research.google.com/pubs/BrainTeam.html ● By releasing TensorFlow, our core machine learning research system, as an open-source project ● By releasing implementations of our research models in TensorFlow ● By collaborating with product teams at Google to get our research into real products What Do We Really Want? ● Build artificial intelligence algorithms and systems that learn from experience ● Use those to solve difficult problems that benefit humanity What do I mean by understanding? What do I mean by understanding? What do I mean by understanding? What do I mean by understanding? Query [ car parts for sale ] What do I mean by understanding? Query [ car parts for sale ] Document 1 … car parking available for a small fee. … parts of our floor model inventory for sale. Document 2 Selling all kinds of automobile and pickup truck parts, engines, and transmissions. Example Needs of the Future ● Which of these eye images shows symptoms of diabetic retinopathy? ● Find me all rooftops in North America ● Describe this video in Spanish -
Big Data Systems Big Data Parallelism
Big Data Systems Big Data Parallelism • Huge data set • crawled documents, web request logs, etc. • Natural parallelism: • can work on different parts of data independently • image processing, grep, indexing, many more Challenges • Parallelize applicaFon • Where to place input and output data? • Where to place computaFon? • How to communicate data? How to manage threads? How to avoid network boJlenecks? • Balance computaFons • Handle failures of nodes during computaFon • Scheduling several applicaFons who want to share infrastructure Goal of MapReduce • To solve these distribuFon/fault-tolerance issues once in a reusable library • To shield the programmer from having to re-solve them for each program • To obtain adequate throughput and scalability • To provide the programmer with a conceptual framework for designing their parallel program Map Reduce • Overview: • ParFFon large data set into M splits • Run map on each parFFon, which produces R local parFFons; using a parFFon funcFon R • Hidden intermediate shuffle phase • Run reduce on each intermediate parFFon, which produces R output files Details • Input values: set of key-value pairs • Job will read chunks of key-value pairs • “key-value” pairs a good enough abstracFon • Map(key, value): • System will execute this funcFon on each key-value pair • Generate a set of intermediate key-value pairs • Reduce(key, values): • Intermediate key-value pairs are sorted • Reduce funcFon is executed on these intermediate key- values Count words in web-pages Map(key, value) { // key is url // value is