A synchronized heterogeneous autoencoder with feature-level and label-level knowledge distillation for the recommendation. (November 2021)

• Record Type: Journal Article
• Authors: Pan, Yiteng; He, Fazhi; Yan, Xiaohu; Li, Haoran
• Is Part Of: Engineering applications of artificial intelligence. Volume 106(2021)
• Publication Date: 2021-11
• Subjects: Deep learning -- Recommender systems -- Denoising autoencoder -- Knowledge distillation; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104494
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Motivated by the success of deep learning techniques, there are numerous deep learning models developed for recommender systems. User-oriented autoencoder (UAE) and item-oriented autoencoder (IAE) are two typical approaches developed for the recommendation by learning features from items or users respectively. Existing works demonstrate that IAE model outperforms UAE model for rating prediction tasks, while UAE model outperforms IAE model for top-N recommendation task. This fact motivates us to develop a new synchronized heterogeneous autoencoder (SHAE) for top-N recommendation by considering both features learned by IAE and UAE models. Especially, we develop two novel heterogeneous knowledge distillation methods in feature-level and label-level to build relations between IAE and UAE models. Compared with matrix factorization approaches, our methods are more efficient with acceptable computational complexity for the recommendation. We conduct comprehensive experiments on public datasets to compare with several state-of-the-art approaches. Experimental results demonstrate that the proposed method significantly outperforms other comparisons for top-N recommendation tasks.