Today's Recommender system is a relatively new area of research in machine learning. The recommender system's main idea is to build relationship between the products, users and make the decision to select the most appropriate product to a specific user. There are four main ways that recommender systems produce a list of recommendations for a user - content-based, Collaborative, Demographic and hybrid filtering. In content-based filtering the model uses specifications of an item in order to recommend additional items with similar properties. Collaborative filtering uses past behavior of the user like items that a user previously viewed or purchased, In summation to any ratings the user gave those items rate and similar conclusions made by other user's items list. To predicts items that the user may find interesting. Demographic filtering is view user profile data like age category, gender, education and living area to find similarities with other profiles to get a new recommender list. Hybrid filtering combines all three filtering techniques. This paper introduces survey about recommendation systems, techniques, challenges the face recommender systems and list some research papers solve these challenges.

Cloud computing appeared for huge data because of its ability to provide users with on-demand, reliable, flexible, and low-cost services. With the increasing use of cloud applications, data security protection has become an important issue for the cloud. In this work, the proposed approach was used to improve the performance of encryption /Decryption file by using AES and OTP algorithms integrated on Hadoop. Where files are encrypted within the HDFS and decrypted within the Map Task. Encryption /Decryption in previous works used AES algorithm, the size of the encrypted file increased by 50% from the original file size. The proposed approach improved this ratio as the size of the encrypted file increased by 20% from the original file size. Also, we have compared this approach with the previously implemented method, we implement this new approach to secure HDFS, and some experimental studies were conducted to verify its effectiveness.

Data growth in recent years has been swift, leading to the emergence of big data science. Distributed File Systems (DFS) are commonly used to handle big data, like Google File System (GFS), Hadoop Distributed File System (HDFS), and others. The DFS should provide the availability of data and reliability of the system in case of failure. The DFS replicates the files in different locations to provide availability and reliability. These replications consume storage space and other resources. The importance of these files differs depending on how frequently they are used in the system. So some of these files do not deserve to replicate many times because it is unimportant in the system. This paper introduces a Dynamic Replication Policy using Machine Learning Clustering (DRPMLC) on HDFS, which uses Machine Learning to cluster the files into different groups and apply other replication policies to each group to reduce the storage consumption, improve the read and write operations time and keep the availability and reliability of HDFS as a High-Performance Distributed Computing (HPDC).