Survey on Recommender System using Hybrid Collaborative Filtering Techniques in Online Social Network

Abstract

The Recommender system [RS] has now become an inseparable feature of Electronic Commerce (E-Commerce) and online social networking websites. It gives recommendations to the users of social networking thus helping them to connect to their social contacts and buyers (technically call users as buyers on e-commerce sites) thus helping them buying certain items of their interests. RS helps in increasing the market of the commercials thus encouraging other commercial industries to adapt the recommender system. It provides recommendations of the items to the users using certain filtering techniques. The paper provides a survey of the techniques and approaches used in the RS and reviews it using collaborative filtering, and similarity measures, furthermore the performance metrics that are used by the collaborative recommender systems to find the similarity among the users or items and to evaluate the performance of the RS are also discussed. The different approaches adopted by the different social networking and e-commercial sites and the problems and limitations of the existing recommender systems are also examined.