Diversified recommendation using implicit content node embedding in heterogeneous information network

Abstract

Many approaches based on Graph Neural Networks (GNNs) have been proposed to identify relationships between users and items while modelling user preferences with significant improvements in recommendation quality. Besides accuracy, diversity in recommendation is often a desirable property for a better user experience in a real-world application. Recently many recommendation techniques based on heterogeneous information networks have been drawing attention to improvement in diversity. However, most such algorithms use re-ranking approaches or diversity regularization (ensemble learning) in a heterogeneous graph network. These approaches often compromise with accuracy to include diversity in the recommendation. The author proposed a novel technique involving both diversity and accuracy at the same time for recommendation generation. Our approach uses implicit user information to generate a low-dimensional embedding representation for each node. The model also includes derived user features for diversity to train the model for diversified recommendation generation. The proposed model iteratively finds infrequently recommended yet relevant items, adds them to the users’ final recommendation lists, and balances the accuracy diversity tradeoff. Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed model, Diverse Heterogeneous Node Embedding Model for Recommendation (Div-HetNEMRec), for diverse recommendations with substantially better coverage and reasonably good improvement in accuracy over the state-of-the-art techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.