Abstract
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.
Abstract (translated)
推荐系统在处理信息过载的各种领域中得到了积极的研究和应用。尽管在电影、音乐和电子商务等领域的推荐系统中有很多研究,但相对较少关注NFT领域的推荐系统,尽管NFT市场持续增长。本文提出了一种用于NFT的推荐系统,该系统利用各种数据源(从NFT交易记录到外部物品特征),生成符合个人偏好的精确推荐。我们开发了一种数据有效的图基推荐系统,能够有效地捕捉每个物品与用户之间的关系,并生成包含节点特征和图结构信息的节点嵌入。此外,我们还利用用户与物品之间的互动之外的其他输入,例如图像特征、文本特征和价格特征。数值实验证实,在利用所有类型的物品特征作为侧信息后,基于图的推荐系统性能显著提高,从而超越了所有其他基线。
URL
https://arxiv.org/abs/2403.18305
