1,295 research outputs found
NFTs to MARS: Multi-Attention Recommender System for NFTs
Recommender systems have become essential tools for enhancing user
experiences across various domains. While extensive research has been conducted
on recommender systems for movies, music, and e-commerce, the rapidly growing
and economically significant Non-Fungible Token (NFT) market remains
underexplored. The unique characteristics and increasing prominence of the NFT
market highlight the importance of developing tailored recommender systems to
cater to its specific needs and unlock its full potential. In this paper, we
examine the distinctive characteristics of NFTs and propose the first
recommender system specifically designed to address NFT market challenges. In
specific, we develop a Multi-Attention Recommender System for NFTs (NFT-MARS)
with three key characteristics: (1) graph attention to handle sparse user-item
interactions, (2) multi-modal attention to incorporate feature preference of
users, and (3) multi-task learning to consider the dual nature of NFTs as both
artwork and financial assets. We demonstrate the effectiveness of NFT-MARS
compared to various baseline models using the actual transaction data of NFTs
collected directly from blockchain for four of the most popular NFT
collections. The source code and data are available at
https://anonymous.4open.science/r/RecSys2023-93ED
ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation
Multimodal recommendation aims to model user and item representations
comprehensively with the involvement of multimedia content for effective
recommendations. Existing research has shown that it is beneficial for
recommendation performance to combine (user- and item-) ID embeddings with
multimodal salient features, indicating the value of IDs. However, there is a
lack of a thorough analysis of the ID embeddings in terms of feature semantics
in the literature. In this paper, we revisit the value of ID embeddings for
multimodal recommendation and conduct a thorough study regarding its semantics,
which we recognize as subtle features of content and structures. Then, we
propose a novel recommendation model by incorporating ID embeddings to enhance
the semantic features of both content and structures. Specifically, we put
forward a hierarchical attention mechanism to incorporate ID embeddings in
modality fusing, coupled with contrastive learning, to enhance content
representations. Meanwhile, we propose a lightweight graph convolutional
network for each modality to amalgamate neighborhood and ID embeddings for
improving structural representations. Finally, the content and structure
representations are combined to form the ultimate item embedding for
recommendation. Extensive experiments on three real-world datasets (Baby,
Sports, and Clothing) demonstrate the superiority of our method over
state-of-the-art multimodal recommendation methods and the effectiveness of
fine-grained ID embeddings
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