Knowledge Enhanced Multi-intent Transformer Network for Recommendation

This paper addresses the challenges of multi-intent modeling and knowledge noise in knowledge-aware recommendation systems. We propose a knowledge-enhanced multi-intent transformer that integrates global heterogeneous information to model user intents while selectively filtering intent-irrelevant knowledge triples. Extensive experiments on multiple benchmark datasets demonstrate consistent improvements over strong baselines.

Recommended citation: Ding Zou, Wei Wei, Feida Zhu, Chuanyu Xu, Tao Zhang, and Chengfu Huo. Knowledge Enhanced Multi-intent Transformer Network for Recommendation. Companion Proceedings of the ACM Web Conference 2024, 1-9.