Abstract
The rapid expansion of information on the Internet has rendered recommender systems vital for mitigating information overload. However, existing recommendation models based on heterogeneous information networks (HINs) often face challenges such as data sparsity and insufficient semantic utilization. Therefore, we propose a multi-layer attention hybrid recommendation model based on heterogeneous information networks (MLAHRec). Compared to traditional HIN-based recommendation models, we design a progressive three-layer attention architecture of “collaborative-node-path.” Specifically, collaborative attention first enhances the direct interaction representation between users and items. Subsequently, node attention filters important neighbor information on the same meta-path. Finally, path attention adaptively fuses the semantics of multiple meta-paths, thereby achieving hierarchical refinement from micro-level interactions to macro-level semantics. Experiments on four real datasets, including MovieLens, LastFM, Yelp, and Douban-Movie, demonstrate that MLAHRec significantly outperforms mainstream baseline algorithms, as determined by Precision@10, Recall@10, and NDCG@10 metrics, validating the effectiveness and interpretability of the model.