Deep heterogeneous autoencoders for Collaborative Filtering

• المؤلفون الرئيسيون: Li, Tianyu, Ma, Yukun, Xu, Jiu, Stenger, Björn, Liu, Chen, Hirate, Yu
• مؤلفون آخرون: School of Computer Science and Engineering
• التنسيق: Conference or Workshop Item
• اللغة: English
• منشور في: 2020
• الموضوعات: Engineering::Computer science and engineering; Deep Autoencoder; Heterogeneous Data
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/144026

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.