AIRec: Attentive Intersection Model for Tag-Aware Recommendation

Abstract Tag-aware recommender systems (TRS) utilize rich tagging information to better depict user portraits and item features. Recently, many efforts have been done to improve TRS with neural networks. However, existing methods construct user representations through either explicit tagging behaviors or implicit interacted items, which is inadequate to capture multi-aspect user preferences. Besides, there are still lacks of investigation about the intersection between user and item tags, which is crucial for better recommendation. In this paper, we propose AIRec, an attentive intersection model for TRS, to address the above issues. More precisely, we first project the sparse tag vectors into a latent space through multi-layer perceptron (MLP). Then, the user representations are constructed with a hierarchical attention network, where the item-level attention differentiates the contributions of interacted items and the preference-level attention discriminates the saliencies between explicit and implicit preferences. After that, the intersection between user and item tags is exploited to enhance the learning of conjunct features. Finally, the user and item representations are concatenated and fed to factorization machines (FM) for score prediction. We conduct extensive experiments on two real-world datasets, demonstrating significant improvements of AIRec over state-of-the-art methods for tag-aware top-n recommendation.