Abstract:
Recommendation systems are software applications or systems that help individual users to find the most relevant products to their needs or tastes. These systems use filtering techniques to generate recommendations. These techniques are categorized majority into collaborative based filtering, content based technique, and hybrid algorithm. In most general terms, Recommendation systems are defined as the techniques used to predict the rating one individual will give to an item or social entity. These items can be books, movies, restaurants and things on which individuals have different preferences. These preferences are being predicted using two approaches first content based approach which involves characteristics of an item and second collaborative filtering approaches which takes into account user's past behavior to make choices. In collaborative filtering, partners are chosen who will make recommendations because they share similar ratings history with the target user. One partner who have similar ratings to the target user may not be a reliable predictor for a particular item. So the past record of the partner of making a reliable recommendation also needs to be take into consideration which is dictated by trustworthiness of a partner. In order to keep track of past records of a recommender reputation systems comes into the picture those who actually assign reputation ratings to the partners. To develop this project the most essential tool is Android Studio which help to simulate all the parts of the work and test it. And Java,Php,Mysql Technology is used for internal logic development. After implementation of all functions, the system is tested in different stages and it works successfully as a prototype.
