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

A Recommender System for NFT Collectibles with Item Feature

Conversational and question-based recommender systems have gained increasing
attention in recent years, with users enabled to converse with the system and
better control recommendations. Nevertheless, research in the field is still
limited, compared to traditional recommender systems. In this work, we propose
a novel Question-based recommendation method, Qrec, to assist users to find
items interactively, by answering automatically constructed and algorithmically
chosen questions. Previous conversational recommender systems ask users to
express their preferences over items or item facets. Our model, instead, asks
users to express their preferences over descriptive item features. The model is
first trained offline by a novel matrix factorization algorithm, and then
iteratively updates the user and item latent factors online by a closed-form
solution based on the user answers. Meanwhile, our model infers the underlying
user belief and preferences over items to learn an optimal question-asking
strategy by using Generalized Binary Search, so as to ask a sequence of
questions to the user. Our experimental results demonstrate that our proposed
matrix factorization model outperforms the traditional Probabilistic Matrix
Factorization model. Further, our proposed Qrec model can greatly improve the
performance of state-of-the-art baselines, and it is also effective in the case
of cold-start user and item recommendations.

本论文提出了一种名为 Qrec 的基于问题的推荐方法，采用新颖的矩阵分解和二进制搜索策略，使得用户可以通过回答问题寻找物品推荐，实现了主动式交互推荐，比传统方法更有效。