Search Engine Similarity Analysis: A Combined Content and Rankings Approach
How different are search engines? The search engine wars are a favorite topic of on-line analysts, as two of the biggest companies in the world, Google and Microsoft, battle for prevalence of the web search space. Differences in search engine popularity can be explained by their effectiveness or other factors, such as familiarity with the most popular first engine, peer imitation, or force of habit. In this work we present a thorough analysis of the affinity of the two major search engines, Google and Bing, along with DuckDuckGo, which goes to great lengths to emphasize its privacy-friendly credentials. To do so, we collected search results using a comprehensive set of 300 unique queries for two time periods in 2016 and 2019, and developed a new similarity metric that leverages both the content and the ranking of search responses. We evaluated the characteristics of the metric against other metrics and approaches that have been proposed in the literature, and used it to (1) investigate the similarities of search engine results, (2) the evolution of their affinity over time, (3) what aspects of the results influence similarity, and (4) how the metric differs over different kinds of search services. We found that Google stands apart, but Bing and DuckDuckGo are largely indistinguishable from each other.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Improving Academic Plagiarism Detection for STEM Documents by Analyzing Mathematical Content and Citations
Identifying academic plagiarism is a pressing task for educational and research institutions, publishers, and funding agencies. Current plagiarism detection systems reliably find instances of copied and moderately reword…
MathQuantifying Similarity: Text-Mining Approaches to Evaluate ChatGPT and Google Bard Content in Relation to BioMedical Literature
Background: The emergence of generative AI tools, empowered by Large Language Models (LLMs), has shown powerful capabilities in generating content. To date, the assessment of the usefulness of such content, generated by …
Prompt EngineeringContent-based Recommendation Engine for Video Streaming Platform
Recommendation engine suggest content, product or services to the user by using machine learning algorithm. This paper proposed a content-based recommendation engine for providing video suggestion to the user based on th…
When Content is Goliath and Algorithm is David: The Style and Semantic Effects of Generative Search Engine
Generative search engines (GEs) leverage large language models (LLMs) to deliver AI-generated summaries with website citations, establishing novel traffic acquisition channels while fundamentally altering the search engi…
Semantic SimilarityArtificial intelligence for Sustainable Energy: A Contextual Topic Modeling and Content Analysis
Parallel to the rising debates over sustainable energy and artificial intelligence solutions, the world is currently discussing the ethics of artificial intelligence and its possible negative effects on society and the e…
EthicsManagement