A Distributed Multi Event Solution for Recommender Systems Using Hadoop

Big data is a phenomenon that takes central stage in industry and academia arising from the advent of online services and mobile applications. Improving the efficiency of data processing and analysis has become a challenging issue. While a number of methods from different communities have been proposed for solving the “Big Data” problems, we worked with multi-event Intelligent Systems that offer efficient mechanisms, which significantly reduce the costs of processing large volume of data and improve data processing quality. Social networks could benefit from recommender systems in order to optimize the queries and ads they display for each special user. Among different approaches to analyze user data and making recommendations, we employed Collaborative Filtering with Cosine Similarity criterion for item-based similarity recognitions. In the implemented method, a Holonic multi-event system (HMES) is designed to process a portion of Amazon database in a distributed manner. The use of Hadoop and map-reduce technology is aimed to make more accurate and faster predictions and recommendations. Different evaluation standards such as Perfect Hit (PHIT), and Mean Percentage Rank (MPR) are used to examine and compare the proposed method with other conventional methods. The results obtained in this thesis are satisfactory compared to the results of the evaluation given in the literature. Keywords: recommender system, hadoop, multi event, artificial intelligence, big data, holonic