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The global interpretation assumes that there exist some fixed set of underlying topics derived from inter-document similarity. These global clusters or their representatives can then be used to relate relevance of two documents (e.g. two documents in the same cluster should both be relevant to the same request). Methods in this spirit include:
In October 2020, PewDiePie was allegedly shadow-banned by YouTube, which led to his channel and videos becoming unavailable on search results. However, YouTube denied shadow-banning him, although the human review was restricted due to the COVID-19 pandemic. YouTube was criticized by PewDiePie himself, his fans, other YouTubers, and netizens ...
Searches on YouTube for Joe Rogan’s Oct. 25 interview of Donald Trump were not “prominently” displaying the original podcast episode — a situation that the Google-owned video giant said it ...
Its underlying assumption is that most users have a general conception of which documents should be denoted as relevant or irrelevant. [1] Therefore, the user's search query is revised to include an arbitrary percentage of relevant and irrelevant documents as a means of increasing the search engine 's recall , and possibly the precision as well.
Rather, search engines and other information retrieval applications are designed to collect and store information (indexing), receive a query from a user, search for and filter relevant information based on that query (searching/filtering), and then present the user with only a subset of those results, which are ranked from most relevant to ...
In the algorithmic ranking model that search engines used in the past, relevance of a site is determined after analyzing the text and content on the page and link structure of the document. In contrast, search results with social search highlight content that was created or touched by other users who are in the Social Graph of the person ...
Ranking of query is one of the fundamental problems in information retrieval (IR), [1] the scientific/engineering discipline behind search engines. [2] Given a query q and a collection D of documents that match the query, the problem is to rank, that is, sort, the documents in D according to some criterion so that the "best" results appear early in the result list displayed to the user.
So it can be looked at as the probability that a relevant document is retrieved by the query. It is trivial to achieve recall of 100% by returning all documents in response to any query. Therefore, recall alone is not enough but one needs to measure the number of non-relevant documents also, for example by computing the precision.