Improving the performance of social networks using parallel distributed computing platform of big data

Jafar.Soleymani¹

1) Iran university

Publication : 2nd. International Congress on science & Engineering - paris(parisconf.com)
Abstract :
the need for a recommender system effective for refining expanding volume of information has increased greatly.In this research to solve the proclaimed problems a recommender system applied that for detailed recommendations to the user, utilize the user comments and apply Spark processing engine in the context of HadOOP. In this thesis a combination of two-step procedure approached to recommend the user. In the first stage, for each item based on its ID, all users comments with regard to the use of dictionaries and dictionary-based approaches to traditional WordNet , classified into classes of positive and negative categories. In the second stage, using algorithms based on cooperation (Collaborative Filtering) and calculating the similarity between users, and active users items, the most similar item to the active user items situated in a list for suggestion.in this stage the previous suggested list with considering attained results from users in the second stage combined and items that earn negative views are omitted from final recommended list for users. The attained results indicate that the present method is more efficient in comparison with usual recommending method.
Keywords : recommended systems Big data Spark SocialNetworks Collaborative Filtering Comments analysis exploring ideas.