A Brief Review on Search Engine Optimization

Sharma, Dheeraj; Shukla, Rishabh; Giri, Anil Kumar; Kumar, Sumit

doi:10.1109/confluence.2019.8776976

Cited by 45 publications

(22 citation statements)

References 8 publications

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“…LinkedIn is a platform for professionals who offer LinkedIn ads, which is a paid ad that will appear in the timeline of its users. According to 2018 data, 65% of B2B marketers have acquired their customers by using LinkedIn ads [29].…”

Section: B Display Advertising -Linkedin Adsmentioning

confidence: 99%

Digital Marketing Strategy to Increase Brand Awareness and Customer Purchase Intention (Case Study: Ailesh Green Consulting)

Putri

2021

EJBMR

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Ailesh Green Consulting is an environmental consulting service with the target is a company that produces waste, especially companies that are included in the gold criteria in PROPER. The main problem is having low brand awareness and the low of customer purchase intention from target customers to use their services. The research methodology uses qualitative research. This research aims to help raise brand awareness and customer purchase intention of their potential customers. The data was collected using an interview system with 10 respondents who had a background in the environmental field, working for companies that are under gold criteria in PROPER. Based on the result, respondents' brand awareness is low, as well as the customer purchase intentions. It was found the digital media recommended to use for the company by the respondents is display advertising, LinkedIn social media, search engines, and websites. Based on the results, Ailesh Green Consulting is able to optimize Search Engine Optimization (SEO) supported by an attractive website optimizer and content marketing website, create and optimize the use of LinkedIn social media, and use of LinkedIn ads.

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Section: B Display Advertising -Linkedin Adsmentioning

confidence: 99%

Digital Marketing Strategy to Increase Brand Awareness and Customer Purchase Intention (Case Study: Ailesh Green Consulting)

Putri

2021

EJBMR

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“…Considering more than 90% of users do not go past the first page of the Google search results (Sharma et al 2019) and the first three items displayed in Amazon search results account for 64% of all clicks (Baker 2018), we think it is of great significance to ensure gender fairness in images ranked top in search results. Therefore, we propose the fairness-greedy algorithm to guarantee gender fairness in the first few pages with high priority.…”

Section: Fairness-greedy Algorithmmentioning

confidence: 99%

Has CEO Gender Bias Really Been Fixed? Adversarial Attacking and Improving Gender Fairness in Image Search

Feng

Shah

2022

AAAI

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Gender bias is one of the most common and well-studied demographic biases in information retrieval, and in general in AI systems. After discovering and reporting that gender bias for certain professions could change searchers' worldviews, mainstreaming image search engines, such as Google, quickly took action to correct and fix such a bias. However, given the nature of these systems, viz., being opaque, it is unclear if they addressed unequal gender representation and gender stereotypes in image search results systematically and in a sustainable way. In this paper, we propose adversarial attack queries composed of professions and countries (e.g., 'CEO United States') to investigate whether gender bias is thoroughly mitigated by image search engines. Our experiments on Google, Baidu, Naver, and Yandex Image Search show that the proposed attack can trigger high levels of gender bias in image search results very effectively. To defend against such attacks and mitigate gender bias, we design and implement three novel re-ranking algorithms -- epsilon-greedy algorithm, relevance-aware swapping algorithm, and fairness-greedy algorithm, to re-rank returned images for given image queries. Experiments on both simulated (three typical gender distributions) and real-world datasets demonstrate the proposed algorithms can mitigate gender bias effectively.

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“…ough it is an excellent technique to define the page ranking of p, spammers exploit this technique to boost their page rank by increasing the inbound links to it. Web spammers often use the so-called Facebook search engine optimisation (SEO) groups, SEO forums, paid-link services, subreddits, and SEO service providers for this reason even though every search engine provides guidelines to website owners for achieving a good rank [35]. Several researchers from academia and industry also worked on different techniques used for link-based spamdexing detection.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Framework for Content‐ and Link‐Based Web‐Spam Detection: A Combined Approach

et al. 2021

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In this modern era, people utilise the web to share information and to deliver services and products. The information seekers use different search engines (SEs) such as Google, Bing, and Yahoo as tools to search for products, services, and information. However, web spamming is one of the most significant issues encountered by SEs because it dramatically affects the quality of SE results. Web spamming’s economic impact is enormous because web spammers index massive free advertising data on SEs to increase the volume of web traffic on a targeted website. Spammers trick an SE into ranking irrelevant web pages higher than relevant web pages in the search engine results pages (SERPs) using different web-spamming techniques. Consequently, these high-ranked unrelated web pages contain insufficient or inappropriate information for the user. To detect the spam web pages, several researchers from industry and academia are working. No efficient technique that is capable of catching all spam web pages on the World Wide Web (WWW) has been presented yet. This research is an attempt to propose an improved framework for content- and link-based web-spam identification. The framework uses stopwords, keywords’ frequency, part of speech (POS) ratio, spam keywords database, and copied-content algorithms for content-based web-spam detection. For link-based web-spam detection, we initially exposed the relationship network behind the link-based web spamming and then used the paid-link database, neighbour pages, spam signals, and link-farm algorithms. Finally, we combined all the content- and link-based spam identification algorithms to identify both types of spam. To conduct experiments and to obtain threshold values, WEBSPAM-UK2006 and WEBSPAM-UK2007 datasets were used. A promising F-measure of 79.6% with 81.2% precision shows the applicability and effectiveness of the proposed approach.

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A Brief Review on Search Engine Optimization

Cited by 45 publications

References 8 publications

Digital Marketing Strategy to Increase Brand Awareness and Customer Purchase Intention (Case Study: Ailesh Green Consulting)

Digital Marketing Strategy to Increase Brand Awareness and Customer Purchase Intention (Case Study: Ailesh Green Consulting)

Has CEO Gender Bias Really Been Fixed? Adversarial Attacking and Improving Gender Fairness in Image Search

An Improved Framework for Content‐ and Link‐Based Web‐Spam Detection: A Combined Approach

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