LASGRec

With the explosion of information, personalized recommender plays a vital role in almost all economic platforms. Usually, the recommender exploits user–item (UI) interactive data to learn users’ latent interests, and then correspondingly conducts recommendations. To address the problem of sparse interactions, graph neural networks (GNNs) have been used to efficiently learn the latent representations of users and items, through structurally modeling the inter-relationships among users, items, and their attributes as graphs. However, most of the existing GNN-based methods ignore the issue of the irrelevant attributes, which means that some attributes of an item are irrelevant to a specific user’s preference. Incorporating them into a recommendation scheme may introduce noise and decrease recommendation accuracy. Thus, to address the issue above, this article proposes a novel personalized recommender based on learnable attribute sampling and heterogeneous graph neural network (LASGRec) to improve the recommender’s performance. The work’s contributions are mainly threefold. First, based on the user’s interactive history with items, the heterogeneous user–item–attribute (UIA) graph is constructed, and attributes of the items are sampled with a learnable neural network to alleviate the issue of irrelevant attributes. Second, using the pruned UIA, the heterogeneous GNN model is appropriately used to learn representations of users and items. Novelly, the learnt user’s embedding first aggregates the sampled attributes of items interactive with the user, and then aggregates these items. The learnt embedding of each item incorporates two relationships: the interacted users and its sampled attributes. Finally, comprehensive experiments on multiple real-world datasets demonstrate the superiority of the proposed LASGRec over the state-of-the-art deep neural network (DNN-) and GNN-based recommendation schemes

Artificial neural network · Data mining · Graph · Machine learning · Recommender system · Advanced Graph Neural Networks · Caching and Content Delivery · Computer Science · Recommender Systems and Techniques · Artificial Intelligence · Theoretical Computer Science

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