CONTEXTUAL MOVIE RECOMMENDER SYSTEM

Abstract

Recommender Systems (RS) are software tool, technique or advanced form of software applications which helps user in decision making process and shows them the product of their interest from huge amount of data. The suggestions generated by user try to narrow down the list of items the user may overlook due of inexperience or huge amount of data. RS are primarily directed towards individuals who lack sufficient personal experience or competence to evaluate the potentially overwhelming number of alternative items that a mobile app may offer. RS takes in information about the user and predicts the rating the user would give the product and shows the product accordingly. That means you can show the user only the things they would like the best and not waste their time with products that are not useful for them. RS try to

predict what the most suitable products or services are, based on the user's preferences and constraints. This project also contains the Contextual based features which means it shows suggestion according to the situation in which the user is interacting with the

application (for example: current location, current time). Hence the project aims to

show the interest-based suggestions to user involving contextual feature to enhance the user experience and to gain the loyalty of user. In order to complete such a computational task, RS collect from users their preferences, which are either explicitly or implicitly expressed e.g., as ratings for products, or are inferred by interpreting user actions or directly take review from user to determine preference. Hereby in this project, we are using FDD techniques of Agile Development Methodology. Feature-driven design (FDD) is an iterative and incremental software development process that follows the principles of the agile manifesto. The idea is to develop the high-level features, scope and domain object model and then use that to plan, design, develop and test the specific requirements and tasks based on the overarching feature that they belong to.