Abstract
Recommender System is a subclass of information filtering system. It identifies similarity among users or items. It can be used as information filtering tool in online social network. Collaborative filtering recommendations are based on similarity of users or items, all data should be compared with each other in order to calculate this similarity. Due to large amount of data in dataset, too much time is required for this calculation, and in these systems, scalability problem is observed. It is better to group data, and each data should be compared with data in the same group. Content based filtering recommends items to users according to users history and items he liked in the past. To improve the working and quality of recommender system, a Hybrid approach by combining content based filtering and collaborative filtering, which includes Memory based (K-Nearest Neighbor) and Model based (clustering and rule based techniques),is presented in our proposed methodology. Using Hybrid approac