Using users With Highprediction Accuracy Incollaborative Filtering Systems
Recommender systems are software tools and techniques that introduce the items according to user needs. Content-based and collaborative filtering methods are the most successful solutions in recommender systems. Content-based method is based on item’s attributes. This method checks the features of user's favourite items and then proposes the items which have the most similar characteristics with those items. Collaborative filtering method is based on the determination of similar items or similar users, which are called item-based and user-based collaborative filtering, respectively. In this thesis a hybrid method is proposed that integrates collaborative filtering and content-based methods. The proposed method can be viewed as user-based Collaborative filtering technique. However to findusers with similar taste with active user as users with high prediction accuracy, content features of the item are used under investigation to put more emphasis on user’s rating for similar items. In other words two users are similar if their ratings are similar on items that have similar context. This is achieved by assigning a weight to each rating when calculating the similarity of two users.