CS 535Fall 2016Semester Long ProjectAssigned on 29 August 2016Final Due on 12 December 2016Total Points: 100This is a semester-long project, encouraging students to dive into the research in data mining. Specifically, this project concerns with the research on developing a recommender system. The project has three phases. The first phase begins at the beginning of the semester when you have received the assignment. You are asked to conduct the research on recommender systems independently by yourself.Recommender systems have been studied extensively in the literature and have alsobeen found popularly useful in many real-world applications. A recommender system is considered in the scenario where we have m different users and n different items in a specific application (e.g., in an E-commerce application we have n different customers potentially intending to buy m different commercial items) where a user i may give an item j a rating value k based on this user’s preference (i ∈[1,... , m]; j ∈[1,...,n]; k∈[1,...,K]). Essentially, the whole user preference rating data can be represented as a matrix of n by m where each element of this matrix is the value k if the user in question gave a rating for the item in question, and 0 if no such rating was given yet. Initially, all the elements of this matrix are 0. After we have collected a certain amount of such user preference rating data, this matrix becomes partially filled out with different non-zero values. The goal of a recommender system is that, after we have collected a certain amount of the user preference rating data, the systemis able to predict the rating value k of user i for item j if user i has not yet given such arating. In other words, given such a partially filled out matrix, a recommender system shall be able to fill out all the predicted rating values for those elements with the current value of 0. That is, a recommender system mathematically is a solution to a matrix value completion problem.In this phase, you dive into the research on recommender systems in the literature andeither develop a new recommender system by yourself or identify an existing, working recommender system. Then you implement and evaluate this implemented system till you are happy with the system. You may use whatever language you are comfortable with to implement the recommender system. Now you are ready to test and report how good your system is. You are given a partially filled out rating data matrix with this assignment. The matrix is given in an ASCII text file where each rowis a non-zero element of this matrix with three entries: the user ID number, the item

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