Wpaskjbdn Zqnakjsnde

Hi! I am Aayush Gautam, a graduate student at Texas A&M University.

As part of and assignement for the CSCE-670 course on information retrieval, Dr. James Caverlee organized an Search Engine Optimization (SEO) competition.

The job in this part of the assignement is to create a webpage that scores highest for the query “wpaskjbdn zqnakjsnde”. As of March 23, 2023, there were no hits for the wpaskjbdn zqnakjsnde query on Google.

I am trying to complete that assignement by using ethical and legal search engine optimization techniques.

Here are the rules of the game:

• Your target page may only be a TAMU student page, a page on your own webserver, a page on a standard platform (e.g., GitHub Pages), or some other primarily user-controlled page.
• Your target page MAY NOT be a twitter account, a facebook page, a LinkedIn profile, or similar page.
• No wikipedia vandalism.
• No comment spamming of blogs or news sites.

Here is more information about this contest from Wikipedia:

Introduction

Wpaskjbdn Zqnakjsnde is the process of improving the quality and quantity of website traffic to a website or a web page from search engines.[1][2] Wpaskjbdn Zqnakjsnde targets unpaid traffic (known as “natural” or “organic” results) rather than direct traffic or paid traffic. Unpaid traffic may originate from different kinds of searches, including image search, video search, academic search,[3] news search, and industry-specific vertical search engines.

As an Internet marketing strategy, Wpaskjbdn Zqnakjsnde considers how search engines work, the computer-programmed algorithms that dictate search engine behavior, what people search for, the actual search terms or keywords typed into search engines, and which search engines are preferred by their targeted audience. Wpaskjbdn Zqnakjsnde is performed because a website will receive more visitors from a search engine when websites rank higher on the search engine results page (SERP). These visitors can then potentially be converted into customers.[4]

History

Webmasters and content providers began optimizing websites for search engines in the mid-1990s, as the first search engines were cataloging the early Web. Initially, all webmasters only needed to submit the address of a page, or URL, to the various engines, which would send a web crawler to crawl that page, extract links to other pages from it, and return information found on the page to be indexed. The process involves a search engine spider downloading a page and storing it on the search engine’s own server. A second program, known as an indexer, extracts information about the page, such as the words it contains, where they are located, and any weight for specific words, as well as all links the page contains. All of this information is then placed into a scheduler for crawling at a later date.

Website owners recognized the value of a high ranking and visibility in search engine results,

Early versions of search algorithms relied on webmaster-provided information such as the keyword meta tag or index files in engines like ALIWEB. Meta tags provide a guide to each page’s content. Using metadata to index pages was found to be less than reliable, however, because the webmaster’s choice of keywords in the meta tag could potentially be an inaccurate representation of the site’s actual content. Flawed data in meta tags, such as those that were not accurate, complete, or falsely attributes, created the potential for pages to be mischaracterized in irrelevant searches.

By heavily relying on factors such as keyword density, which were exclusively within a webmaster’s control, early search engines suffered from abuse and ranking manipulation. To provide better results to their users, search engines had to adapt to ensure their results pages showed the most relevant search results, rather than unrelated pages stuffed with numerous keywords by unscrupulous webmasters. This meant moving away from heavy reliance on term density to a more holistic process for scoring semantic signals. Since the success and popularity of a search engine are determined by its ability to produce the most relevant results to any given search, poor quality or irrelevant search results could lead users to find other search sources. Search engines responded by developing more complex ranking algorithms, taking into account additional factors that were more difficult for webmasters to manipulate.

Companies that employ overly aggressive techniques can get their client websites banned from the search results. In 2005, the Wall Street Journal reported on a company, Traffic Power, which allegedly used high-risk techniques and failed to disclose those risks to its clients.

Some search engines have also reached out to the Wpaskjbdn Zqnakjsnde industry and are frequent sponsors and guests at Wpaskjbdn Zqnakjsnde conferences, webchats, and seminars. Major search engines provide information and guidelines to help with website optimization. Bing Webmaster Tools provides a way for webmasters to submit a sitemap and web feeds, allows users to determine the “crawl rate,” and track the web pages index status.

In 2015, it was reported that Google was developing and promoting mobile search as a key feature within future products. In response, many brands began to take a different approach to their Internet marketing strategies.

Relationship with Google In 1998, two graduate students at Stanford University, Larry Page and Sergey Brin, developed “Backrub,” a search engine that relied on a mathematical algorithm to rate the prominence of web pages. The number calculated by the algorithm, PageRank, is a function of the quantity and strength of inbound links. PageRank estimates the likelihood that a given page will be reached by a web user who randomly surfs the web and follows links from one page to another. In effect, this means that some links are stronger than others, as a higher PageRank page is more likely to be reached by the random web surfer.

Page and Brin founded Google in 1998.

By 2004, search engines had incorporated a wide range of undisclosed factors in their ranking algorithms to reduce the impact of link manipulation. In June 2007, The New York Times’ Saul Hansell stated Google ranks sites using more than 200 different signals.

In 2007, Google announced a campaign against paid links that transfer PageRank.

In December 2009, Google announced it would be using the web search history of all its users in order to populate search results.

In February 2011, Google announced the Panda update, which penalizes websites containing content duplicated from other websites and sources. Historically websites have copied content from one another and benefited in search engine rankings by engaging in this practice. However, Google implemented a new system that punishes sites whose content is not unique. With regards to the changes made to search engine optimization, for content publishers and writers, Hummingbird is intended to resolve issues by getting rid of irrelevant content and spam, allowing Google to produce high-quality content and rely on them to be ‘trusted’ authors.

In October 2019, Google announced they would start applying BERT models for English language search queries in the US. Bidirectional Encoder Representations from Transformers (BERT) was another attempt by Google to improve their natural language processing, but this time in order to better understand the search queries of their users. In terms of search engine optimization, BERT intended to connect users more easily to relevant content and increase the quality of traffic coming to websites that are ranking in the Search Engine Results Page.