Z-ENG: Graph based Recommender
Graph based recommendation techniques deliver high quality recommendations, e.g., random walk, recommendation spreading. However, these calculation methods are eager on memory and CPU/GPU and suffer from performance issues. The goal of this project is to reduce the size of the knowledge graph while keeping the relevant information in order to improve the performance of calculation techniques. The student will apply representation learning techniques to the adjacency matrix and the graph Laplacian and evaluate various personalization algorithms.