Optimization Matrix Factorization Recommendation Algorithm Based on Rating Centrality

Matrix factorization (MF) is extensively used to mine the user preference from explicit ratings in recommender systems. However, the reliability of explicit ratings is not always consistent, because many factors may affect the user's final evaluation on an item, including commercial advertising and a friend's recommendation. Therefore, mining the reliable ratings of user is critical to further improve the performance of the recommender system. In this work, we analyze the deviation degree of each rating in overall rating distribution of user and item, and propose the notion of user-based rating centrality and item-based rating centrality, respectively. Moreover, based on the rating centrality, we measure the reliability of each user rating and provide an optimized matrix factorization recommendation algorithm. Experimental results on two popular recommendation datasets reveal that our method gets better performance compared with other matrix factorization recommendation algorithms, especially on sparse datasets

A Survey of Matrix Completion Methods for Recommendation Systems

In recent years, the recommendation systems have become increasingly popular and have been used in a broad variety of applications. Here, we investigate the matrix completion techniques for the recommendation systems that are based on collaborative filtering. The collaborative filtering problem can be viewed as predicting the favorability of a user with respect to new items of commodities. When a rating matrix is constructed with users as rows, items as columns, and entries as ratings, the collaborative filtering problem can then be modeled as a matrix completion problem by filling out the unknown elements in the rating matrix. This article presents a comprehensive survey of the matrix completion methods used in recommendation systems. We focus on the mathematical models for matrix completion and the corresponding computational algorithms as well as their characteristics and potential issues. Several applications other than the traditional user-item association prediction are also discussed

Software library for stream-based recommender systems

Tradicionalmente, um algoritmo de machine learning é capaz de aprender com dados, dado um conjunto tratado e construído anteriormente. Também é possível analisar esse conjunto de dados, usando técnicas de mineração de dados e extrair conclusões a partir dele. Ambos os conceitos têm inúmeras aplicações em todo o mundo, desde diagnósticos médicos até reconhecimento de fala ou mesmo consultas a mecanismos de pesquisa. No entanto, tradicionalmente, supõe-se que o conjunto de dados esteja disponível a todo o momento. Isso não é necessariamente verdade com os dados modernos pois os aplicativos de sistemas distribuídos recebem e processam milhões de fluxos de dados em uma fração de tempo limitado. Portanto, são necessárias técnicas para extrair e processar esses fluxos de dados, em um período de tempo limitado, com bons resultados e dimensionamento eficaz à medida que os dados aumentam. Um sistema específico de análise e previsão de conclusões futuras a partir de dados fornecidos são os sistemas de recomendação. Vários serviços online usam sistemas de recomendação para fornecer conteúdo personalizado a seus usuários. Em muitos casos, as recomendações são um dos geradores de tráfego mais eficazes nesses serviços. O problema reside em encontrar o melhor pequeno subconjunto de itens em um sistema que corresponda às preferências pessoais de cada usuário, através da análise de uma quantidade muito grande de dados históricos. Esse problema recebe mais atenção se for considerado um problema genérico, não específico, ou seja, se uma biblioteca for construída para que possa ser estendida e usada como uma ferramenta para ajudar a construir um sistema para um caso de uso específico. Podem-se distinguir soluções entre perfeitas ou estatisticamente semelhantes. Devido a grande quantidade de dados disponíveis, a decisão de reprocessar todos os dados, sempre que novos dados chegam, não seria viável; portanto, algoritmos incrementais são usados ​​para processar os dados recebidos e manter o modelo de recomendação atualizado. O objetivo real deste trabalho é implementar uma biblioteca que contenha e avalie essas abordagens incrementais para recomendações de que as atuais são específicas da tarefa.Traditionally, a machine learning algorithm is able to learn from data, given a previously built and treated data set. One can also analyze that data set, using data mining techniques, and draw conclusions from it. Both of these concepts have numerous world-wide applications, from medical diagnosis to speech recognition or even search engine queries. However, traditionally speaking, it is being assumed that the data set is available at all times. That is not necessarily true with modern data, as distributed systems applications receive and process millions of data streams on a limited time fraction. Therefore, there is a need for techniques to mine and process these data streams,on a limited time period with good results and effective scaling as data grows. One specific use case of analyzing and predicting future conclusions from given data, are recommendation systems.Several online services use recommender systems to deliver personalized content to their users.In many cases, recommendations are one of the most effective traffic generators in such services.The problem lies in finding the best small subset of items in a system that matches the personal preferences of each user, through the analysis of a very large amount of historical data. This problem gets more attention if it is considered as a generic problem, not as a specific one, that is,if a library is built so that it can be extended and used as a tool to help build a system for a specific use case. One can distinguish solutions between perfect ones or statistically similar ones. Due to the large amount of data available, the decision to reprocess all the data every time new data arrives, would not be feasible so, incremental algorithms are used to process incoming data and keeping the recommendation model updated. The real purpose of this work is to implement such a library which contains, and evaluates these incremental approaches to recommendation since current ones are task-specific