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Owing to the superiority of GNN in learning on graph data and its efficacy in capturing collaborative signals and sequential patterns, utilizing GNN techniques in recommender systems has gain increasing interests in academia and industry. In this survey, we provide a comprehensive review of the most recent works on GNN-based recommender systems. We proposed a classification scheme for organizing existing works. For each category, we briefly clarify the main issues, and detail the corresponding strategies adopted by the representative models. We also discuss the advantages and limitations of the existing strategies. Furthermore, we suggest several promising directions for future researches. We hope this survey can provide readers with a general understanding of the recent progress in this field, and shed some light on future developments.
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results. Thus, it becomes critical to embrace a trustworthy recommender
Given the convenience of collecting information through online services, recommender systems now consume large scale data and play a more important role in improving user experience. With the recent emergence of Graph Neural Networks (GNNs), GNN-base
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS employ advanced graph learning approaches to model users preferences and intentions as well as items characteristics for r
Among various recommender techniques, collaborative filtering (CF) is the most successful one. And a key problem in CF is how to represent users and items. Previous works usually represent a user (an item) as a vector of latent factors (aka. textit{e
Modern recommender systems (RS) work by processing a number of signals that can be inferred from large sets of user-item interaction data. The main signal to analyze stems from the raw matrix that represents interactions. However, we can increase the