Collaborative filtering is generally used as a recommendation system. There is a huge growth in the amount of data on the web. This recommendation system helps users to choose books on the web, which is the most suitable for users. Collaborative collects previous user information about an item such as books, films, music, ideas, and so on. To recommend the best items. The recommendation system serves as a bridge gap between the user and the application or website by providing many options from which users make their choice of interests.
Personalized recommendations help users get a list of items that interest them on the web. Most recommendation systems use collaborative filtering techniques to produce recommendations to users. In this final project uses items based collaborative filtering method. In the item based collaborative filtering method, calculate similarity using the adjusted cosine similarity equation, after obtaining the similarity value between books, then look for the predicted value of a product that has not been rated by the user by using the weighted sum equation. In this final project, the two equations above are used and to measure the accuracy of the predictions produced by this technique.
Keywords: Recommended System, Item-Based Collaborative Filtering