· PDF 檔案
Collaborative Filtering Matrix Factorization Neural Collaborative Filtering 5 Introduction 1. Embedding , which transforms users and items to vectorized representations 2. Interaction modeling , which reconstructs historical interactions based on the embeddings.
Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user’s (or an item’s) embedding by mapping from pre-existing features that describe the user (or the item), such as ID and attributes. We argue that an
· PDF 檔案
Neural Graph Collaborative Filtering High-order Propagation We stack more embedding propagation layers to explore the high-order connectivity information. representation of