Human Vs. Machine Intelligence Real Estate Photo Editing

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omarfaruk007acri
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Human Vs. Machine Intelligence Real Estate Photo Editing

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home page article SEO Human vs. Machine Intelligence: How to Win When "Duplicate" Content Is Unique Human vs. Machine Intelligence: How to Win When "Duplicate" Content Is Real Estate Photo Editing Unique Published: 2020-11-19 Machine learning and algorithm-based intelligence are impressive, but often lack what is natural to humans: common sense. It is well known that if you put the same content on multiple pages, the Real Estate Photo Editing content will be duplicated. But what if you create a page about similar things that have important differences? The algorithm flags them as duplicates, but humans have no problem distinguishing pages like these. E-Commerce:

Similar products with multiple variations or significant differences Travel: Hotel Branch, Destination Package with Similar Content Job Ads: Comprehensive List of Same Items Business: A page of a local branch that offers the same service in different regions How does this happen? How can I find the problem? What can you do about it? Risk of duplicate content Duplicate content interferes with your ability to Real Estate Photo Editing display your site to search users in the following ways: Unintentionally competing unique page rankings for the same keyword are lost Unable to rank pages in the cluster because Google chose one page as legitimate Loss of site authority for large amounts of thin content How machines identify duplicate content Google uses an algorithm to determine if two pages or parts of a page are duplicate content. This is defined by Google as "quite similar" content. Google's similarity Real Estate Photo Editing detection is based on a patented Simhash algorithm that analyzes blocks of content on web pages. It then calculates a unique identifier for each block and creates a hash or "fingerprint" for each page.

Scalability is important because the number of web pages is Real Estate Photo Editing huge. Simhash is currently the only viable way to find duplicate content on a large scale. The Simhash fingerprint is: It's cheap to calculate. They are established with a single crawl of the page. The fixed length makes comparisons easy. You can almost find duplicates. Unlike many other algorithms, it equates small page changes with Real Estate Photo Editing small hash changes. This finally means that the difference between any two fingerprints can be measured algorithmically and expressed as a percentage. To reduce the cost of evaluating every pair of pages, Google uses the following techniques: Clustering: By grouping a set of pages that are sufficiently similar, everything else is already classified as different, so only the fingerprints in the cluster need to be compared. Estimate: For very large clusters, the average similarity is app
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