Hello, this is a c++ term project . There are train.csv and test.csv files we should make best recommendations to the user, and i want to do it in user based collaborative filtering system, but it is so hard to write code. Can you write me a code for this project. I'm trying to do this almost 1 week but i couldn't do it. Thanks in advance

1 The Problem Recommender systems help people find items of interest by making personalized recommendations according to their preferences. For example, a recommender system can make personalized recommendations of items such as movies, books, hotels, or music to people. In order to model users' preferences their past interactions such as product views, ratings, and purchases are used. In the recommender systems area of rescarch (which is a subficld of imachime learning and information retrieval) different algorithms have been developed which are currently being used by many large companies. In this project you will implement neighborhood based collaborative filtering (NBCF) algorithms in order to make predictions for movic ratings of people. There are two main types of NBCF algorithms: user-based (UBCF) and item-based (IBCF). Let us describe each with an example dataset. 2 UBCF and IBCF One of the fundamental problems in recommender systems is to prediet the rating of a user for a particular item. For example, given the dataset in Table 1, what might be the rating of User 2 for Movie 3 ? If we can predict this rating then, if it is a high value like 4 or 5 , we can decide to recommend Movie 3 to User 2. Table 1: An example dataset. In its simplest, form, UBCF works as follows: in order to predict the rating of user u to item i, we first find the most similar k users to u (who rated i ) and predict the rating as the average ratings of these most similar k users on item i. For finding the similarity between two users different methods can be used. In collaborative filtering we look at the ratings of other users and find users which have similar ratings. One popular method to find such a similarity is called cosine similarity which is given below: cosine(A,B)=ABAB=i=1nAi2i=1nBi2i=1nAiBi