{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tower-bridge-net-tb-net-bidirectional","title":"Tower Bridge Net (TB-Net): Bidirectional Knowledge Graph Aware Embedding Propagation for Explainable Recommender Systems","arxiv_id":null,"date":"2022-08-02","proceeding":"International Conference on Data Engineering 2022 8","authors":["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"],"abstract":"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.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9835387","url_pdf":"https://ieeexplore.ieee.org/abstract/document/9835387","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"tower-bridge-net-tb-net-bidirectional","repo_url":"https://github.com/2023-MindSpore-1/ms-code-30","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"tower-bridge-net-tb-net-bidirectional","repo_url":"https://github.com/d294270681/tbnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"explainable-recommendation","task_name":"Explainable Recommendation"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[{"method_slug":null,"method_name":null}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}