Shuffling a Stacked Deck: The Case for Partially Randomized Ranking of Search Engine Results

In-degree, PageRank, number of visits and other measures of Web page popularity significantly influence the ranking of search results by modern search engines. The assumption is that popularity is closely correlated with quality, a more elusive concept that is difficult to measure directly. Unfortunately, the correlation between popularity and quality is very weak for newly-created pages that have yet to receive many visits and/or in-links. Worse, since discovery of new content is largely done by querying search engines, and because users usually focus their attention on the top few results, newly-created but high-quality pages are effectively ``shut out,'' and it can take a very long time before they become popular. We propose a simple and elegant solution to this problem: the introduction of a controlled amount of randomness into search result ranking methods. Doing so offers new pages a chance to prove their worth, although clearly using too much randomness will degrade result quality and annul any benefits achieved. Hence there is a tradeoff between exploration to estimate the quality of new pages and exploitation of pages already known to be of high quality. We study this tradeoff both analytically and via simulation, in the context of an economic objective function based on aggregate result quality amortized over time. We show that a modest amount of randomness leads to improved search results

News Recommender Systems with Feedback

The focus of present research is widely used news recommendation techniques such as “most popular” or “most e-mailed”. In this paper we have introduced an alternative way of recommendation based on feedback. Various notable properties of the feedback based recommendation technique have been also discussed. Through simulation model we show that the recommendation technique used in the present research allows implementers to have a flexibility to make a balance between accuracy and distortion. Analytical results have been established in a special case of two articles using the formulation based on generalized urn models. Finally, we show that news recommender systems can be also studied through two armed bandit algorithms

Citation Statistics

This is a report about the use and misuse of citation data in the assessment of scientific research. The idea that research assessment must be done using ``simple and objective'' methods is increasingly prevalent today. The ``simple and objective'' methods are broadly interpreted as bibliometrics, that is, citation data and the statistics derived from them. There is a belief that citation statistics are inherently more accurate because they substitute simple numbers for complex judgments, and hence overcome the possible subjectivity of peer review. But this belief is unfounded.Comment: This paper commented in: [arXiv:0910.3532], [arXiv:0910.3537], [arXiv:0910.3543], [arXiv:0910.3546]. Rejoinder in [arXiv:0910.3548]. Published in at http://dx.doi.org/10.1214/09-STS285 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Facebook text posts classification with TensorFlow

Natural language processing (NLP) is one of the most important technologies of the XXI century. Machine Comprehension is a very interesting but challenging task in both Natural Language Processing (NLP) and artificial intelligent (AI) research. NLP can be applied wherever human-machine interaction is needed. Recently, deep learning methods show good results in tasks involving NLP. Standard models can often be used to solve a range of tasks, without the need to apply traditional analytical engineering techniques. The widespread distribution of social networks and the large number of users could give us impressive results, which can further build system interests analysis with a large number of established trust relationships. In this article, we will consider the task of classifying texts in relation to the object under study using the TensorFlow framework.Обробка природних мов (ОПМ) є однією з найважливіших технологій XXI століття. Машинне розуміння є дуже цікавим, але складним завданням як в обробці природних мов (ОПМ), так і в дослідженні штучного інтелекту (ШІ). ОПМ можна застосовувати там, де потрібна взаємодія людини з машиною (людино-машинна взаємодія). Останнім часом глибокі методи навчання показують вражаючі результати в вирішенні завдань, що стосуються ОПМ. Стандартні моделі глибокого навчання часто можуть використовуватися для вирішення цілого ряду завдань без необхідності застосування традиційних аналітичних інженерних методів, що потребують надзвичайно багато ресурсів. У цій статті ми розглянемо завдання класифікації текстів по відношенню до досліджуваного об'єкта за допомогою фреймворка «TensorFlow». Remove selectedОбработка естественных языков (ОЕЯ) является одной из важнейших технологий XXI века. Машинное понимание очень интересное, но сложное задание как в обработке естественных языков (ОЕЯ), так и в исследовании искусственного интеллекта (ИИ). ОЕЯ можно применять там, где требуется взаимодействие человека с машиной (человеко-компьютерное взаимодействие). В последнее время глубокие методы обучения показывают впечатляющие результаты в решении задач, касающихся ОЕЯ. Стандартные модели глубокого обучения часто могут использоваться для решения целого ряда задач, без необходимости применения традиционных аналитических инженерных методов, требующих очень много ресурсов. В этой статье мы рассмотрим задачи классификации текстов по отношению к исследуемому объекту с помощью фреймворка «TensorFlow»