KNN-Based Collaborative Filtering for Fine-Grained Intelligent Grad-School Recommendation System

The development of the Internet has led to information overload, and how to filter and sift information is a rigorous requirement in all fields. In response to this challenge, recommendation systems have emerged as a valuable tool, offering personalized content and services by efficiently searching and processing dynamically generated information. For students applying to grad schools, finding relevant information can be time-consuming and unreliable from official websites or forums. In light of these challenges, we present a novel solution in the form of an application recommendation platform. Our proposed platform leverages specific open-source datasets and real-time information from platform users using KNN (K-Nearest Neighbor) and CF (Collaborative Filtering) techniques to provide recommendations based on users’ individual backgrounds, we aim to reduce the complexity inherent in information retrieval while simultaneously enhancing the relevance of the recommendations delivered to users. Specifically, we first collect user behavior data, then we will construct the data model and perform some preprocessing on it. Calculate the user similarity, and find out the K-nearest neighbors and rate based on K-nearest neighbors, finally, the recommendation engine is used to calculate the highest-rated items to be recommended to the users.