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 of 3–4% AUC over strong baselines.