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Download or read book The Great American 2020 Election Steal written by Paula Beverage and published by Dorrance Publishing. This book was released on 2023-05-18 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Great American 2020 Election Steal by Paula Beverage About the Book The Great American 2020 Election Steal Vol. 1 is a tongue-in-cheek explanation of the insanity of people who want to be the boss or our leaders, along with the absurdity of MSM, Silicon Valley, and Hollywood. It’s an outsider’s perspective about the 2020 election steal from President Trump and the American people who voted legally for him. These thieves are so brash; we pay for what “they” do to us. The falsehood(s) and hypocrisy with how “they” prostitute themselves in public and want respect “they” have not earned. It would be humorous if it wasn’t so true. It’s a high-stakes game of life with its double standards and double jeopardy between the haves and the have-nots. George Carlin: It’s a big club and we ain’t in it... About the Author Paula Beverage has a GED, a job, and a black belt in hard-knock lessons and is an independent, self-made realist. She has spent her life being a mother and caregiver for disabled persons within her family and extended family, giving her the up-close and personal experience with alphabet agents and agencies as well as crooked courts, administrators, regulators, and “officials.”

Download or read book Context Aware Ranking with Factorization Models written by Steffen Rendle and published by Springer Science & Business Media. This book was released on 2010-11-11 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: Context-aware ranking is an important task with many applications. E.g. in recommender systems items (products, movies, ...) and for search engines webpages should be ranked. In all these applications, the ranking is not global (i.e. always the same) but depends on the context. Simple examples for context are the user for recommender systems and the query for search engines. More complicated context includes time, last actions, etc. The major problem is that typically the variable domains (e.g. customers, products) are categorical and huge, the observations are very sparse and only positive events are observed. In this book, a generic method for context-aware ranking as well as its application are presented. For modelling a new factorization based on pairwise interactions is proposed and compared to other tensor factorization approaches. For learning, the `Bayesian Context-aware Ranking' framework consisting of an optimization criterion and algorithm is developed. The second main part of the book applies this general theory to the three scenarios of item, tag and sequential-set recommendation. Furthermore extensions of time-variant factors and one-class problems are studied. This book generalizes and builds on work that has received the `WWW 2010 Best Paper Award', the `WSDM 2010 Best Student Paper Award' and the `ECML/PKDD 2009 Best Discovery Challenge Award'.

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