A Recommender System for NFT Collectibles with Item Feature

• URL: http://arxiv.org/abs/2403.18305v2
• Date: Wed, 3 Apr 2024 06:52:50 GMT
• Title: A Recommender System for NFT Collectibles with Item Feature
• Authors: Minjoo Choi, Seonmi Kim, Yejin Kim, Youngbin Lee, Joohwan Hong, Yongjae Lee,
• Abstract summary: This paper presents a recommender system for NFTs that utilize a variety of data sources to generate precise recommendations. We develop a data-efficient graph-based recommender system to efficiently capture the complex relationship between each item and users.
• Score: 24.201581738408045
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Attention-based sequential recommendation system using multimodal data [8.110978727364397]
  We propose an attention-based sequential recommendation method that employs multimodal data of items such as images, texts, and categories. The experimental results obtained from the Amazon datasets show that the proposed method outperforms those of conventional sequential recommendation systems.
  arXiv  Detail & Related papers  (2024-05-28T08:41:05Z)
• Preference and Concurrence Aware Bayesian Graph Neural Networks for Recommender Systems [5.465420718331109]
  Graph-based collaborative filtering methods have prevailing performance for recommender systems. We propose an efficient generative model that jointly considers the preferences of users, the concurrence of items and some important graph structure information.
  arXiv  Detail & Related papers  (2023-11-30T11:49:33Z)
• Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation [71.5871100348448]
  ColdGPT models item-attribute correlations into an item-attribute graph by extracting fine-grained attributes from item contents. ColdGPT transfers knowledge into the item-attribute graph from various available data sources, i.e., item contents, historical purchase sequences, and review texts of the existing items. Extensive experiments show that ColdGPT consistently outperforms the existing SCS recommenders by large margins.
  arXiv  Detail & Related papers  (2023-06-26T07:04:47Z)
• NFTs to MARS: Multi-Attention Recommender System for NFTs [20.74874765792172]
  We develop a Multi-Attention Recommender System for NFTs (NFT-MARS) NFT-MARS has three key characteristics: graph attention to handle sparse user-item interactions, multi-modal attention to incorporate feature preference of users, and multi-task learning to consider the dual nature of NFTs as both artwork and financial assets.
  arXiv  Detail & Related papers  (2023-06-13T11:53:24Z)
• FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings [108.77460689459247]
  We propose a Federated Graph Recommender System (FedGRec) to mitigate privacy concerns. In our system, users and the server explicitly store latent embeddings for users and items, where the latent embeddings summarize different orders of indirect user-item interactions. We perform extensive empirical evaluations to verify the efficacy of using latent embeddings as a proxy of missing interaction graph.
  arXiv  Detail & Related papers  (2022-10-25T01:08:20Z)
• ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest [60.841761065439414]
  At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations in all shopping use cases. This approach has led to significant improvements in engagement and conversion metrics, while reducing both infrastructure and maintenance cost.
  arXiv  Detail & Related papers  (2022-05-24T02:28:58Z)
• An Analysis of the Features Considerable for NFT Recommendations [0.0]
  This research explores the methods that NFTs can be recommended to people who interact with NFT-marketplaces. The outcome highlights the necessity of using multiple Recommender Systems to present the user with the best possible NFTs when interacting with decentralized systems.
  arXiv  Detail & Related papers  (2022-05-01T12:11:17Z)
• Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation [56.12499090935242]
  This work proposes a Knowledge-Enhanced Hierarchical Graph Transformer Network (KHGT) to investigate multi-typed interactive patterns between users and items in recommender systems. KHGT is built upon a graph-structured neural architecture to capture type-specific behavior characteristics. We show that KHGT consistently outperforms many state-of-the-art recommendation methods across various evaluation settings.
  arXiv  Detail & Related papers  (2021-10-08T09:44:00Z)
• DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN [59.160401038969795]
  We propose differentiable sampling on Knowledge Graph for Recommendation with GNN (DSKReG) We devise a differentiable sampling strategy, which enables the selection of relevant items to be jointly optimized with the model training procedure. The experimental results demonstrate that our model outperforms state-of-the-art KG-based recommender systems.
  arXiv  Detail & Related papers  (2021-08-26T16:19:59Z)
• Graph Trend Networks for Recommendations [34.06649831739749]
  The key of recommender systems is to predict how likely users will interact with items based on their historical online behaviors. To exploit these user-item interactions, there are increasing efforts on considering the user-item interactions as a user-item bipartite graph. Despite their success, most existing GNN-based recommender systems overlook the existence of interactions caused by unreliable behaviors. We propose the Graph Trend Networks for recommendations (GTN) with principled designs that can capture the adaptive reliability of the interactions.
  arXiv  Detail & Related papers  (2021-08-12T06:09:18Z)
• A Survey on Knowledge Graph-Based Recommender Systems [65.50486149662564]
  We conduct a systematical survey of knowledge graph-based recommender systems. We focus on how the papers utilize the knowledge graph for accurate and explainable recommendation. We introduce datasets used in these works.
  arXiv  Detail & Related papers  (2020-02-28T02:26:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.