Recommender systems have been actively studied and applied in various domains to deal with information overload. Although there are numerous studies on recommender systems for movies, music, and e-commerce, comparatively less attention has been paid to the recommender system for NFTs despite the continuous growth of the NFT market. This paper presents a recommender system for NFTs that utilizes a variety of data sources, from NFT transaction records to external item features, to generate precise recommendations that cater to individual preferences. We develop a data-efficient graph-based recommender system to efficiently capture the complex relationship between each item and users and generate node(item) embeddings which incorporate both node feature information and graph structure. Furthermore, we exploit inputs beyond user-item interactions, such as image feature, text feature, and price feature. Numerical experiments verify the performance of the graph-based recommender system improves significantly after utilizing all types of item features as side information, thereby outperforming all other baselines.

本文介绍了一种用于NFT的推荐系统，利用了各种数据来源，从NFT交易记录到外部项目特征，生成符合个人偏好的精确推荐。我们开发了一种数据高效的基于图的推荐系统，以有效捕捉每个项目与用户之间复杂的关系并生成节点（项目）嵌入，融合了节点特征信息和图结构。此外，我们还利用用户与项目之间的交互之外的输入，如图像特征、文本特征和价格特征。数值实验证实，在利用所有类型的项目特征作为辅助信息之后，基于图的推荐系统的性能显著提高，优于所有其他基准。

一种基于物品特征的NFT藏品推荐系统