Improvement of recommender systems considering big data of users’ comments on chosen items

Regarding to the increase in the online social networks services during the recent years, the recommender system has turned into an emerging research subject. Currently, regarding to the fast and consistent expansion of using the internet, the necessity of a recommender system for refining the large volume of data has increased greatly. The purpose of recommender systems is to provide a list of the interested items for the user and due to the increase in the current data volume, the previous used tools are nor suitable for processing this data volume; hence, having a system which can save and process the large data has turned into a problem. In this study, to solve the mentioned problems, a system is recommended using a model-based collaborative filtering refinement model which uses the Spark processing model in Hadoop context in order for more exact advising the user from the viewpoint of the users. The obtained results indicate that the current method will be more efficient and effective compared to the common recommendation methods.Keywords: recommender systems; big data; sentiment analysis; hadoop; spark

A paper recommender system based on user’s profile in big data scholarly

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  user by the user profile. Findings indicate that suggested approach outperformsthe similar approaches.Keywords: recommender system; bigdata; user profile; content-based recommender system; hadoo

A contextual information based scholary paper recommender system using big data platform

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 (mapping – reducing 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.Keywords: Recommender systems; big data; Hadoop; contextual informatio