Papers › Tower Bridge Net (TB-Net): Bidirectional Knowledge Graph Aware Embedding Propagation...
Tower Bridge Net (TB-Net): Bidirectional Knowledge Graph Aware Embedding Propagation for Explainable Recommender Systems
Shendi Wang, Haoyang Li, Caleb Chen Cao, Xiao-Hui Li, Ng Ngai Fai, Jianxin Liu, Xun Xue, Hu Song, Jinyu Li, Guangye Gu, Lei Chen
Recently, neural networks based models have been widely used for recommender systems (RS). Unfortunately, the existing neural network based RS solutions are often treated as black-boxes, which gain little trust and confidence from users. Thus, there is an increasing demand of explainability. Several explainable recommendation methods have been introduced to RS. However, there is a trade-off between explainability and performance among these methods. In this paper, we propose a novel framework, the Tower Bridge Net (TB-Net), using the proposed bidirectional embedding propagation approach to achieve both superior recommendation and explainability performances. Extensive validation on three public datasets shows that the performance of TB-Net dominates the state-of-the-art models. We quantitatively evaluate the explainability by using numerical metrics and experimentally prove that TB-Net achieves a significant improvement on explainability compared with existing methods. More importantly, TB-Net has been deployed and offers explainable recommendation service for the largest bank in China, Industrial and Commercial Bank of China Limited (ICBC). Results on a billion-scale dataset (1.2 billion nodes and edges) from ICBC show that TB-Net can provide both accurate recommendations and semantic explanations, and is very effective and deployable in practice.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections