Deep Heterogeneous Autoencoders for Collaborative Filtering
This work addresses data sparsity for recommender systems, but it is incremental as it builds on existing autoencoder methods with heterogeneous data.
The paper tackles the data sparsity problem in recommender systems by using heterogeneous auxiliary information like item descriptions and purchase history, resulting in improved mean average precision and recall over state-of-the-art methods on MovieLens and e-commerce datasets.
This paper leverages heterogeneous auxiliary information to address the data sparsity problem of recommender systems. We propose a model that learns a shared feature space from heterogeneous data, such as item descriptions, product tags and online purchase history, to obtain better predictions. Our model consists of autoencoders, not only for numerical and categorical data, but also for sequential data, which enables capturing user tastes, item characteristics and the recent dynamics of user preference. We learn the autoencoder architecture for each data source independently in order to better model their statistical properties. Our evaluation on two MovieLens datasets and an e-commerce dataset shows that mean average precision and recall improve over state-of-the-art methods.