ICCVAE: Item Concept Causal Variational Auto-Encoder for top-n recommendation

Jingyun Feng; Qianqian Wang; Zhejun Huang; Lili Yang

10.1109/ICSP58490.2023.10248832

2023

2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP)

979-8-3503-0246-2

2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP)

908-913

21-23 April 2023

Xi'an, China

Recently, recommendation systems, especially interpretable ones, have become increasingly popular. The recommendation system provides personalized recommendations to users based on their previous behaviour data. Existing approaches often transfer methods in disentangle learning, especially Variational Auto-Encoder (VAE) framework, into the recommendation system. However, VAE is proved sub-optimal due to the independence factor assumption. Unfortunately, few research focuses on the VAE framework without an independent assumption for recommendation system. To escape from sub-optimality from independence assumption and provide more interpretability, in this paper, we propose a new method for top-n recommendation tasks called Item Concept Causal Variational Auto-Encoder (ICCVAE), enabling to build up causal structure for factors, i.e., item concepts in recommendation system. We conduct experiments to prove superiority for ICCVAE. Our framework can reach up to 5.3%, 3.4%, and 6.1% definite improvement over baselines in terms of Recall@20, Recall@50 and NDCG@100.

Recommendation System Variational Auto-Encoder Interpretability

第一

EI

20234314942598

Learning Systems ; Signal Encoding

Information Theory And Signal Processing:716.1 ; Computer Applications:723.5

IEEE

Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China

Jingyun Feng,Qianqian Wang,Zhejun Huang,et al. ICCVAE: Item Concept Causal Variational Auto-Encoder for top-n recommendation[C],2023:908-913.