Cold-start, warm-start and everything in between: An autoencoder based approach to recommendation
Angshul Majumdar, Anant Jain · 2017
This work addresses the problem of cold and warm start arising in recommender systems. Usually a latent factor model based on matrix factorization is used for collaborative filtering (warm start recommender system). Only in recent times, a handful of papers have been published that uses autoencoders for the same task; these studies have shown to yield better results than matrix factorization. This is the first work that proposes a comprehensive autoencoder based formulation to address both the cold and warm start problem. It makes use of both available rating's of users on items as well as associated user and item metadata. The proposed method has been compared with state-of-the-art methods and have shown to supersede them.