Recommendation Systems or Recommender Systems have become widely popular due to surge of information at present time and consumer centric environment. Researchers have looked into a wide range of recommendation systems leveraging a wide range of algorithms. This study investigates three popular recommendation systems in existence, Collaborative Filtering, Content-Based Filtering, and Hybrid recommendation system. The famous MovieLens dataset was utilized for the purpose of this study. The evaluation looked into both quantitative and qualitative aspects of the recommendation systems. We found that from both the perspectives, the hybrid recommendation system performs comparatively better than standalone Collaborative Filtering or Content-Based Filtering recommendation system

M. Tech. (Department of Information and Communication Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / Since their emergence in the 1990’s, recommendation systems have transformed the intelligence of both the web and humans. A pool of research papers has been published in various domains of recommendation systems. These include content based, collaborative and hybrid filtering recommendation systems. Recommendation systems suggest items to users and their principal purpose is to increase sales and recommend items that are predicted to be suitable for users. They achieve this through making calculations based on data that is available on the system. In this study, we give evidence that the research on group recommendation systems must look more carefully at the dynamics of group decision-making in order to produce technologies that will be more beneficial for groups based on the individual interests of group members while also striving to maximise satisfaction. The matrix factorization algorithm of collaborative filtering was used to make predictions and three movie recommendation for each and every individual user. The three recommendations were of three highest predicted movies above the pre-set threshold which was three. Thereafter, four virtual groups of varied sizes were formed based on four highest predicted movies of the users in the dataset. Plurality voting strategy was used to achieve this. A publicly available dataset based on Group Recommender Systems Enhanced by Social Elements, constructed by Lara Quijano from the Group of Artificial Intelligence Applications (GIGA), was used for experiments. The developed recommendation system was able to successfully make individual movie recommendations, generate virtual groups, and recommend movies to these respective groups. The system was evaluated for accuracy in making predictions and it was able to achieve 0.7027 MAE and 0.8996 RMSE. This study was able to recommend to virtual groups to enable social network group members to engage in discussions of recommended items. The study encourages members in engaging in similar activities in their respective physical locations and then discuss on social network.