Abstract-In electronic commerce, recommender systems are used to help customers to choose products according to their needs. These systems suggest products automatically to users by learning their requirements. Recommendations provided by these systems depends upon users purchase probability and preferences. In this paper, different techniques used for recommender systems are studied.Keywords-Recommendation Systems, E-commerce, Content Based Filtering, Collaborative Filtering, Hybrid Methods I. INTRODUCTIONRecommender systems touch our lives every day, from searching on Google to shopping on at any major online retailer. Their sophisticated algorithms attempt and often succeed at showing us the information and products we seek. The information consumers give to service providers and retailers is expanding rapidly, which makes recommendation systems both more complex, and potentially more powerful. Online behaviour including customer metadata, transaction histories and communications allows companies to understand shoppers better, sense their similarities, and address needs they may not even know they have. Simultaneously, we are better able to analyse information about the items sought, including images, sounds in the case of music, and descriptions, which allows companies to refine the ways they cluster products, as well as whom they target as potential buyers. Recommendation systems are at the center of retail both online and off, showing ads on web sites as well as through dynamic displays in brick and mortar stores. By using facial recognition and in-store video cameras, retailers are able to group their customers instantly by gender, age and other demographics in order to show them immediately the goods they are most likely to buy. Given the explosion of consumer goods as well as the rapid increase in the number of vendors on the Internet, the main problem facing customers is how to find the object that they seek, when it is buried beneath a mountain of irrelevant information. A recommender system that can instantly personalize ads in order to solve the customer's search problem is doing both the customer, and the vendor, a favour. New goods are constantly introduced, and new fads sweep the nation, altering shoppers' behaviour. Only a recommender system that is constantly able to learn new patterns can serve consumer needs.

Due to continuously increase of fraud in whole world, affects the daily life of people's and also economic conditions of each country. Different modern fraud detection techniques have been applied to prevent frauds in various fields. In today's world, education is one of the famous fields where cases of fraud admissions, fraud degree certificates are occurring continuously. Fraudsters are continuously playing with life of students. However many techniques have been applied but still it needs to be improve. In this paper, the survey of different fraud detection techniques has been presented. The goal of this paper is to review different existing techniques used to detect frauds.