Local Collaborative Autoencoders
Minjin Choi, Yoonki Jeong, Joonseok Lee, Jongwuk Lee
Abstract
This work presents a generalized local factor model, namely Local Collaborative Autoencoders (LOCA). To our knowledge, it is the first generalized framework under the local low-rank assumption that builds on the neural recommendation models. We explore a large number of local models by adopting a generalized framework with different weight schemes for training and aggregating them. Besides, we develop a novel method of discovering a sub-community to maximize the coverage of local models. Our experimental results demonstrate that LOCA is highly scalable, achieving state-of-the-art results by outperforming existing AE-based and local latent factor models on several large-scale public benchmarks.