The Core Aspects of Search Engine Optimisation Necessary to Move up the Ranking

2011 ◽  
Vol 3 (4) ◽  
pp. 62-70 ◽  
Author(s):  
Stephen O’Neill ◽  
Kevin Curran

Search engine optimization (SEO) is the process of improving the visibility, volume and quality of traffic to website or a web page in search engines via the natural search results. SEO can also target other areas of a search, including image search and local search. SEO is one of many different strategies used for marketing a website but SEO has been proven the most effective. An Internet marketing campaign may drive organic search results to websites or web pages but can be involved with paid advertising on search engines. All search engines have a unique way of ranking the importance of a website. Some search engines focus on the content while others review Meta tags to identify who and what a web site’s business is. Most engines use a combination of Meta tags, content, link popularity, click popularity and longevity to determine a sites ranking. To make it even more complicated, they change their ranking policies frequently. This paper provides an overview of search engine optimisation strategies and pitfalls.

Author(s):  
Stephen O’Neill ◽  
Kevin Curran

Search engine optimization (SEO) is the process of improving the visibility, volume and quality of traffic to website or a web page in search engines via the natural search results. SEO can also target other areas of a search, including image search and local search. SEO is one of many different strategies used for marketing a website but SEO has been proven the most effective. An Internet marketing campaign may drive organic search results to websites or web pages but can be involved with paid advertising on search engines. All search engines have a unique way of ranking the importance of a website. Some search engines focus on the content while others review Meta tags to identify who and what a web site’s business is. Most engines use a combination of Meta tags, content, link popularity, click popularity and longevity to determine a sites ranking. To make it even more complicated, they change their ranking policies frequently. This paper provides an overview of search engine optimisation strategies and pitfalls.


Author(s):  
Xiannong Meng ◽  
Song Xing

This chapter reports the results of a project attempting to assess the performance of a few major search engines from various perspectives. The search engines involved in the study include the Microsoft Search Engine (MSE) when it was in its beta test stage, AllTheWeb, and Yahoo. In a few comparisons, other search engines such as Google, Vivisimo are also included. The study collects statistics such as the average user response time, average process time for a query reported by MSE, as well as the number of pages relevant to a query reported by all search engines involved. The project also studies the quality of search results generated by MSE and other search engines using RankPower as the metric. We found MSE performs well in speed and diversity of the query results, while weaker in other statistics, compared to some other leading search engines. The contribution of this chapter is to review the performance evaluation techniques for search engines and use different measures to assess and compare the quality of different search engines, especially MSE.


2019 ◽  
Vol 16 (9) ◽  
pp. 3712-3716
Author(s):  
Kailash Kumar ◽  
Abdulaziz Al-Besher

This paper examines the overlapping of the results retrieved between three major search engines namely Google, Yahoo and Bing. A rigorous analysis of overlap among these search engines was conducted on 100 random queries. The overlap of first ten web page results, i.e., hundred results from each search engine and only non-sponsored results from these above major search engines were taken into consideration. Search engines have their own frequency of updates and ranking of results based on their relevance. Moreover, sponsored search advertisers are different for different search engines. Single search engine cannot index all Web pages. In this research paper, the overlapping analysis of the results were carried out between October 1, 2018 to October 31, 2018 among these major search engines namely, Google, Yahoo and Bing. A framework is built in Java to analyze the overlap among these search engines. This framework eliminates the common results and merges them in a unified list. It also uses the ranking algorithm to re-rank the search engine results and displays it back to the user.


Author(s):  
Chandran M ◽  
Ramani A. V

<p>The research work is about to test the quality of the website and to improve the quality by analyzing the hit counts, impressions, clicks, count through rates and average positions. This is accomplished using WRPA and SEO technique. The quality of the website mainly lies on the keywords which are present in it. The keywords can be of a search query which is typed by the users in the search engines and based on these keywords, the websites are displayed in the search results. This research work concentrates on bringing the particular websites to the first of the search result in the search engine. The website chosen for research is SRKV. The research work is carried out by creating an index array of Meta tags. This array will hold all the Meta tags. All the search keywords for the website from the users are stored in another array. The index array is matched and compared with the search keywords array. From this, hit to count is calculated for the analysis. Now the calculated hit count and the searched keywords will be analyzed to improve the performance of the website. The matched special keywords from the above comparison are included in the Meta tag to improve the performance of the website. Again all the Meta tags and newly specified keywords in the index array are matched with the SEO keywords. If this matches, then the matched keyword will be stored for improving the quality of the website. Metrics such as impressions, clicks, CTR, average positions are also measured along with the hit counts. The research is carried out under different types of browsers and different types of platforms. Queries about the website from different countries are also measured. In conclusion, if the number of the clicks for the website is more than the average number of clicks, then the quality of the website is good. This research helps in improvising the keywords using WRPA and SEO and thereby improves the quality of the website easily.</p>


2002 ◽  
Vol 63 (4) ◽  
pp. 354-365 ◽  
Author(s):  
Susan Augustine ◽  
Courtney Greene

Have Internet search engines influenced the way students search library Web pages? The results of this usability study reveal that students consistently and frequently use the library Web site’s internal search engine to find information rather than navigating through pages. If students are searching rather than navigating, library Web page designers must make metadata and powerful search engines priorities. The study also shows that students have difficulty interpreting library terminology, experience confusion discerning difference amongst library resources, and prefer to seek human assistance when encountering problems online. These findings imply that library Web sites have not alleviated some of the basic and long-range problems that have challenged librarians in the past.


2013 ◽  
Vol 303-306 ◽  
pp. 2311-2316
Author(s):  
Hong Shen Liu ◽  
Peng Fei Wang

The structures and contents of researching search engines are presented and the core technology is the analysis technology of web pages. The characteristic of analyzing web pages in one website is studied, relations between the web pages web crawler gained at two times are able to be obtained and the changed information among them are found easily. A new method of analyzing web pages in one website is introduced and the method analyzes web pages with the changed information of web pages. The result of applying the method shows that the new method is effective in the analysis of web pages.


2021 ◽  
Vol 13 (1) ◽  
pp. 9
Author(s):  
Goran Matošević ◽  
Jasminka Dobša ◽  
Dunja Mladenić

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.


Author(s):  
Vijay Kasi ◽  
Radhika Jain

In the context of the Internet, a search engine can be defined as a software program designed to help one access information, documents, and other content on the World Wide Web. The adoption and growth of the Internet in the last decade has been unprecedented. The World Wide Web has always been applauded for its simplicity and ease of use. This is evident looking at the extent of the knowledge one requires to build a Web page. The flexible nature of the Internet has enabled the rapid growth and adoption of it, making it hard to search for relevant information on the Web. The number of Web pages has been increasing at an astronomical pace, from around 2 million registered domains in 1995 to 233 million registered domains in 2004 (Consortium, 2004). The Internet, considered a distributed database of information, has the CRUD (create, retrieve, update, and delete) rule applied to it. While the Internet has been effective at creating, updating, and deleting content, it has considerably lacked in enabling the retrieval of relevant information. After all, there is no point in having a Web page that has little or no visibility on the Web. Since the 1990s when the first search program was released, we have come a long way in terms of searching for information. Although we are currently witnessing a tremendous growth in search engine technology, the growth of the Internet has overtaken it, leading to a state in which the existing search engine technology is falling short. When we apply the metrics of relevance, rigor, efficiency, and effectiveness to the search domain, it becomes very clear that we have progressed on the rigor and efficiency metrics by utilizing abundant computing power to produce faster searches with a lot of information. Rigor and efficiency are evident in the large number of indexed pages by the leading search engines (Barroso, Dean, & Holzle, 2003). However, more research needs to be done to address the relevance and effectiveness metrics. Users typically type in two to three keywords when searching, only to end up with a search result having thousands of Web pages! This has made it increasingly hard to effectively find any useful, relevant information. Search engines face a number of challenges today requiring them to perform rigorous searches with relevant results efficiently so that they are effective. These challenges include the following (“Search Engines,” 2004). 1. The Web is growing at a much faster rate than any present search engine technology can index. 2. Web pages are updated frequently, forcing search engines to revisit them periodically. 3. Dynamically generated Web sites may be slow or difficult to index, or may result in excessive results from a single Web site. 4. Many dynamically generated Web sites are not able to be indexed by search engines. 5. The commercial interests of a search engine can interfere with the order of relevant results the search engine shows. 6. Content that is behind a firewall or that is password protected is not accessible to search engines (such as those found in several digital libraries).1 7. Some Web sites have started using tricks such as spamdexing and cloaking to manipulate search engines to display them as the top results for a set of keywords. This can make the search results polluted, with more relevant links being pushed down in the result list. This is a result of the popularity of Web searches and the business potential search engines can generate today. 8. Search engines index all the content of the Web without any bounds on the sensitivity of information. This has raised a few security and privacy flags. With the above background and challenges in mind, we lay out the article as follows. In the next section, we begin with a discussion of search engine evolution. To facilitate the examination and discussion of the search engine development’s progress, we break down this discussion into the three generations of search engines. Figure 1 depicts this evolution pictorially and highlights the need for better search engine technologies. Next, we present a brief discussion on the contemporary state of search engine technology and various types of content searches available today. With this background, the next section documents various concerns about existing search engines setting the stage for better search engine technology. These concerns include information overload, relevance, representation, and categorization. Finally, we briefly address the research efforts under way to alleviate these concerns and then present our conclusion.


Author(s):  
Sakher Khalil Alqaaidi

Search engines save copies of crawled web pages to provide instant search results, saved pages may become old and un-updated as original pages change providing new information and new links, and most of websites don’t submit these new changes to search engines so search engines don’t depend mainly on websites techniques of submitting changes. Keeping pages fresh and updated in search engine is important for giving real page ranks and for providing real time information. Techniques were invented to improve the page update process by search engine. In this paper the author combines two of good known techniques and implements the new one via experiments that improve better results in different experiment cases.


2013 ◽  
Vol 284-287 ◽  
pp. 3375-3379
Author(s):  
Chun Hsiung Tseng ◽  
Fu Cheng Yang ◽  
Yu Ping Tseng ◽  
Yi Yun Chang

Most Web users today rely heavily on search engines to gather information. To achieve better search results, some algorithms such as PageRank have been developed. However, most Web search engines employ keyword-based search and thus have some natural weaknesses. Among these problems, a well-known one is that it is very difficult for search engines to infer semantics from user queries and returned results. Hence, despite of efforts of ranking search results, users may still have to navigate through a huge amount of Web pages to locate the desired resources. In this research, the researchers developed a clustering-based methodology to improve the performance of search engines. Instead of extracting features used for clustering from the returned documents, the proposed method extracts features from the delicious service, which is actually a tag provider service. By utilizing such information, the resulting system can benefit from crowd intelligence. The obtained information is then used for enhancing the performance of the ordinary k-means algorithm to achieve better clustering results.


Sign in / Sign up

Export Citation Format

Share Document