A Network Resource Allocation Recommendation Method with An Improved Similarity Measure

Recommender systems have been acknowledged as efficacious tools for managing information overload. Nevertheless, conventional algorithms adopted in such systems primarily emphasize precise recommendations and, consequently, overlook other vital aspects like the coverage, diversity, and novelty of items. This approach results in less exposure for long-tail items. In this paper, to personalize the recommendations and allocate recommendation resources more purposively, a method named PIM+RA is proposed. This method utilizes a bipartite network that incorporates self-connecting edges and weights. Furthermore, an improved Pearson correlation coefficient is employed for better redistribution. The evaluation of PIM+RA demonstrates a significant enhancement not only in accuracy but also in coverage, diversity, and novelty of the recommendation. It leads to a better balance in recommendation frequency by providing effective exposure to long-tail items, while allowing customized parameters to adjust the recommendation list bias

Red Light Green Light Method for Solving Large Markov Chains

Discrete-time discrete-state finite Markov chains are versatile mathematical models for a wide range of real-life stochastic processes. One of most common tasks in studies of Markov chains is computation of the stationary distribution. Without loss of generality, and drawing our motivation from applications to large networks, we interpret this problem as one of computing the stationary distribution of a random walk on a graph. We propose a new controlled, easily distributed algorithm for this task, briefly summarized as follows: at the beginning, each node receives a fixed amount of cash (positive or negative), and at each iteration, some nodes receive `green light' to distribute their wealth or debt proportionally to the transition probabilities of the Markov chain; the stationary probability of a node is computed as a ratio of the cash distributed by this a node to the total cash distributed by all nodes together. Our method includes as special cases a wide range of known, very different, and previously disconnected methods including power iterations, Gauss-Southwell, and online distributed algorithms. We prove exponential convergence of our method, demonstrate its high efficiency, and derive scheduling strategies for the green-light, that achieve convergence rate faster than state-of-the-art algorithms

Red Light Green Light Method for Solving Large Markov Chains

Discrete-time discrete-state finite Markov chains are versatile mathematical models for a wide range of real-life stochastic processes. One of most common tasks in studies of Markov chains is computation of the stationary distribution. We propose a new general controlled, easily distributed algorithm for this task. The algorithm includes as special cases a wide range of known, very different, and previously disconnected methods including power iterations, versions of Gauss-Southwell formerly restricted to substochastic matrices, and online distributed algorithms. We prove exponential convergence of our method, demonstrate its high efficiency, and derive straightforward control strategies that achieve convergence rates faster than state-of-the-art algorithms.</p

Sumarizador de avaliações usando textrank e modelagem de tópicos

Over the past decade, the Internet has changed the way people work, shop and socialize. Those changes resulted in the increase of User Generated Content (UGC) such as: ratings, reviews, wikis, and videos. UCG contains relevant information for decision-making, especially with regard to the acquisition of goods and services. However, the large volume and dispersion of this content makes it difficult to obtain relevant information. Text summarization appears as a way to make this content more accessible to people. A summary A can be considered better than another B when A is shorter than B while maintaining the same content relevance, or when A, despite being longer, presents more relevant content. Analyzing the literature, we observed that it is possible to produce better quality summaries than those produced by algorithms that correspond to the state of the art in text summarization. We present a multilingual automatic text summarizer that combines and extends the algorithms Latent Dirichlet Allocation (LDA) and TextRank. Our approach, when compared to the state of the art, generates better text summaries in terms of size and content relevance.Na última década a Internet mudou o modo como as pessoas trabalham, fazem compras e se socializam. Essas mudanças resultaram em um aumento no Conteúdo Gerado pelos Usuários (CGU) como, por exemplo: avaliações, notas, artigos e vídeos. Os CGUs possuem informações relevantes para a tomada de decisão, especialmente no que se refere à aquisição de bens e serviços. Entretanto, o grande volume e dispersão deste conteúdo torna difícil a obtenção de informações relevantes. Neste contexto, a sumarização de textos é apresentada como um modo de tornar este conteúdo mais acessível às pessoas. Um dado sumário A pode ser considerado melhor que um outro sumário B se o primeiro for mais curto que o segundo com o mesmo conteúdo, ou quando mesmo sendo mais longo, possui mais informações relevantes. Analisando a literatura disponível, foi constatado que é possível produzir sumários de melhor qualidade do que aqueles que correspondem ao estado da arte em sumarização de textos. Neste trabalho, apresentamos um sumarizador automático multilingual que combina e expande os algoritmos Latent Dirichlet Allocation (LDA) e TextRank. Em comparação com o estado da arte, este trabalho gerou sumários melhores em termos de tamanho e conteúdo