Computers promise that be as a repository of knowledge and wisdom, but instead, they sent us large amounts of data, web mining is the process of information discovery and knowledge from the Web data. The data is collected from the server, client, proxy server or database in Web mining. Web mining methods are divided into three categories: web content mining, web structure mining and web usage mining. There are several functional areas including e-commerce web mining, text mining, and management of customer behavior. Web mining research focuses on developing knowledge extraction techniques which are used for data analysis. 3 main methods that are used for data mining in web include: association or association rules, sequential patterns, and clustering requirements. The main objective of the web mining is to collect information about the user navigation patterns. Of course, web mining is faced with various challenges and constraints. And many researches are currently doing research in the field of web mining that aim to solve this problem.
Social networks can include anything ranging from family, friends, classes, objects and other similar cases, important and effective members, members of exception, the formation of such networks can be discovered by using the relationships between the members of the network which are important to business and research works, to achieve these cases , social networks should be analyzed using special tools. Social network analysis tools generally includes two packaged based on graphical user interfaces (GUIs) and packages made for programming / scripting. These tools are powerful and extensible and are able to analyze big data networks and visualize networks, isolated or central data and other important data can be simply discovered by data visualization. In this paper, some of the most important tools of social network analysis are presented and compared according to some their capabilities.
Recommender systems for research papers have been increasingly popular.In the past 14 years more than 170 research papers,patents and webpageshave been published in this field.Scientific papers recommender systemsare trying to provide some recommendations to each user which are consistent with the users' personal interests based on performance, personal tastes and users behaviors.Since the volume of papers are growing day after day and the recommender systemshave not the ability for covering these huge volumes ofprocessing papers according to the users' preferences it is necessary to use parallel processing (mappingreducing programming) for covering and fast processing of these volumes of papers. The suggested system for this research constitutes a profile for each paper which contains context information and the scope of paper. Then, the system will advise some papers to the user according to the user work domain and the papers domain. For implementing the system it has been used hadoop bed and the parallel programming because the volume of data was a part of a big data and the time was also an important factor. The performance of the suggested system was measured by the criteria such as user satisfaction and the accuracy and the results have been satisfactory.
Users encounter a huge volume of papers in digital libraries and paper search engines such as IEEE Explore, ACM Digital library, Google scholar and etc. these high number of papers make some difficulties for researchers for finding proper information and items.Recommender systems contain successful tools for knowledge of users and identification of their priorities. These systems present a personalized proposal to users who seek to find a special kind of relevant data or their priorities through the big number of data.Recommendersystem based on personalization uses the user profile and in view of the fact that the user profile encompass information pertaining to the user priorities; so it is a very active district in data recovery. Recommendersystem is an attitude that presented in order to encounter difficulties caused by abundant data and it helps users to attain their goals quickly through huge number of data. In this paper, we have presented an approach that received entire of available information in a paper, and formed a profile for each user by short and long inquiries; this profile is personalized for user and then recommends the closest paperto the Recommender systems contain successful tools for knowledge of users and identification of their priorities. These systems present a personalized proposal to users who seek to find a special kind of relevant data or their priorities through the big number of data.Recommendersystem based on personalization uses the user profile and in view of the fact that the user profile encompass information pertaining to the user priorities; so it is a very active district in data recovery. Recommendersystem is an attitude that presented in order to encounter difficulties caused by abundant data and it helps users to attain their goals quickly through huge number of data. In this paper, we have presented an approach that received entire of available information in a paper, and formed a profile for each user by short and long inquiries; this profile is personalized for user and then recommends the closest paperto the Recommender systems contain successful tools for knowledge of users and identification of their priorities. These systems present a personalized proposal to users who seek to find a special kind of relevant data or their priorities through the big number of data.Recommendersystem based on personalization uses the user profile and in view of the fact that the user profile encompass information pertaining to the user priorities; so it is a very active district in data recovery. Recommendersystem is an attitude that presented in order to encounter difficulties caused by abundant data and it helps users to attain their goals quickly through huge number of data. In this paper, we have presented an approach that received entire of available information in a paper, and formed a profile for each user by short and long inquiries; this profile is personalized for user and then recommends the closest paperto the J FundamAppl Sci. 2016, 8(2S), 942-955 942 user...
To face the problem of information overload, digital libraries, like other businesses, have used recommender systems and try to personalize recommendations to users by using the textual information of papers. This textual information includes title, abstract, keywords, publisher, author and other similar items. Since the volume of papers is increasing day by day and recommender systems do not have the ability to cover this huge volume to process papers according to the user’s tastes, that is why we need to use our papers to cover and process this volume quickly. We have big data tools, which will offer relevant recommendations by running parallel processing. In this chapter, the researches and researches of researchers in the field of recommender systems/aware of the text of scientific papers and recommender systems have been discussed.
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