Hybrid Item-Item Recommendation via Semi-Parametric Embedding

Hybrid Item-Item Recommendation via Semi-Parametric Embedding

Peng Hu, Rong Du, Yao Hu, Nan Li

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 2521-2527. https://doi.org/10.24963/ijcai.2019/350

Nowadays, item-item recommendation plays an important role in modern recommender systems. Traditionally, this is either solved by behavior-based collaborative filtering or content-based meth- ods. However, both kinds of methods often suffer from cold-start problems, or poor performance due to few behavior supervision; and hybrid methods which can leverage the strength of both kinds of methods are needed. In this paper, we propose a semi-parametric embedding framework for this problem. Specifically, the embedding of an item is composed of two parts, i.e., the parametric part from content information and the non-parametric part designed to encode behavior information; meanwhile, a deep learning algorithm is proposed to learn two parts simultaneously. Extensive experiments on real-world datasets demonstrate the effectiveness and robustness of the proposed method.
Keywords:
Machine Learning: Ensemble Methods
Multidisciplinary Topics and Applications: Recommender Systems