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Recommender systems can be helpful for individuals to make well-informed decisions in complex financial markets. While many studies have focused on predicting stock prices, even advanced models fall short of accurately forecasting them. Additionally, previous studies indicate that individual investors often disregard established investment theories, favoring their personal preferences instead. This presents a challenge for stock recommendation systems, which must not only provide strong investment performance but also respect these individual preferences. To create effective stock recommender systems, three critical elements must be incorporated: 1) individual preferences, 2) portfolio diversification, and 3) the temporal dynamics of the first two. In response, we propose a new model, Portfolio Temporal Graph Network Recommender, PfoTGNRec, which can handle time-varying collaborative signals and incorporates diversification-enhancing sampling. On real-world individual trading data, our approach demonstrates superior performance compared to state-of-the-art baselines, including cutting-edge dynamic embedding models and existing stock recommendation models. Indeed, we show that PfoTGNRec is an effective solution that can balance customer preferences with the need to suggest portfolios with high Return-on-Investment. The source code and data are available at https://anonymous.4open.science/r/ICAIF2024-E23E.

This paper explores the utilization of Temporal Graph Networks (TGN) for financial anomaly detection, a pressing need in the era of fintech and digitized financial transactions. We present a comprehensive framework that leverages TGN, capable of capturing dynamic changes in edges within financial networks, for fraud detection. Our study compares TGN's performance against static Graph Neural Network (GNN) baselines, as well as cutting-edge hypergraph neural network baselines using DGraph dataset for a realistic financial context. Our results demonstrate that TGN significantly outperforms other models in terms of AUC metrics. This superior performance underlines TGN's potential as an effective tool for detecting financial fraud, showcasing its ability to adapt to the dynamic and complex nature of modern financial systems. We also experimented with various graph embedding modules within the TGN framework and compared the effectiveness of each module. In conclusion, we demonstrated that, even with variations within TGN, it is possible to achieve good performance in the anomaly detection task., Comment: Presented at the AAAI 2024 Workshop on AI in Finance for Social Impact (https://sites.google.com/view/aifin-aaai2024)

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., Comment: Presented at the AAAI 2023 Bridge on AI for Financial Services (https://sites.google.com/view/aaai-ai-fin/home)

Recommender systems, crucial for user engagement on platforms like e-commerce and streaming services, often lag behind users' evolving preferences due to static data reliance. After Temporal Graph Networks (TGNs) were proposed, various studies have shown that TGN can significantly improve situations where the features of nodes and edges dynamically change over time. However, despite its promising capabilities, it has not been directly applied in recommender systems to date. Our study bridges this gap by directly implementing Temporal Graph Networks (TGN) in recommender systems, a first in this field. Using real-world datasets and a range of graph and history embedding methods, we show TGN's adaptability, confirming its effectiveness in dynamic recommendation scenarios., Comment: Presented at the AAAI 2024 Workshop on Recommendation Ecosystems: Modeling, Optimization and Incentive Design

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.

Due to its high electrical conductivity and excellent transmittance at terahertz frequencies, graphene is a promising candidate as transparent electrodes for terahertz devices. We demonstrate a liquid crystal based terahertz phase shifter with the graphene films as transparent electrodes. The maximum phase shift is 10.8 degree and the saturation voltage is 5 V with a 50 um liquid crystal cell. The transmittance at terahertz frequencies and electrical conductivity depending on the number of graphene layer are also investigated. The proposed phase shifter provides a continuous tunability, fully electrical controllability, and low DC voltage operation.

Shifting of the surface plasmon resonance wavelength induced by the variation of the thickness of insulating spacer between single layer graphene and Au nanoparticles is studied. The system demonstrates a blue shift of 29 nm as the thickness of the spacer layer increases from 0 to 15 nm. This is due to the electromagnetic coupling between the localized surface plasmons excited in the nanoparticles and the graphene film. The strength of the coupling decays exponentially with a decay length of d/R=0.36, where d is the spacer layer thickness and R is the diameter of the Au nanoparticles. The result agrees qualitatively well with the plasmon ruler equation. Interestingly, a further increment of the spacer layer thickness induces a red shift of 17 nm in the resonance wavelength and the shift saturates when the thickness of the spacer layer increases above 20 nm.

Tunability of the surface plasmon resonance wavelength is demonstrated by varying the thickness of Al2O3 spacer layer inserted between the graphene and nanoparticles. By varying the spacer layer thickness from 0.3 to 1.8 nm, the resonance wavelength is shifted from 583 to 566 nm. The shift is due to a change in the electromagnetic field coupling strength between the localized surface plasmons excited in the gold nanoparticles and a single layer graphene film. In contrast, when the graphene film is absent from the system, no noticeable shift in the resonance wavelength is observed upon varying the spacer thickness.

We report that 30-inch scale multiple roll-to-roll transfer and wet chemical doping considerably enhance the electrical properties of the graphene films grown on roll-type Cu substrates by chemical vapor deposition. The resulting graphene films shows a sheet resistance as low as ~30 Ohm/sq at ~90 % transparency which is superior to commercial transparent electrodes such as indium tin oxides (ITO). The monolayer of graphene shows sheet resistances as low as ~125 Ohm/sq with 97.4% optical transmittance and half-integer quantum Hall effect, indicating the high-quality of these graphene films. As a practical application, we also fabricated a touch screen panel device based on the graphene transparent electrodes, showing extraordinary mechanical and electrical performances.

We developed means to produce wafer scale, high-quality graphene films as large as 3 inch wafer size on Ni and Cu films under ambient-pressure and transfer them onto arbitrary substrates through instantaneous etching of metal layers. We also demonstrated the applications of the large-area graphene films for the batch fabrication of field-effect transistor (FET) arrays and stretchable strain gauges showing extraordinary performances. Transistors showed the hole and electron mobilities of the device of 1,100 cm2/Vs and 550 cm2/Vs at drain bias of -0.75V, respectively. The piezo-resistance gauge factor of strain sensor was ~6.1. These methods represent a significant step toward the realization of graphene devices in wafer scale as well as application in optoelectronics, flexible and stretchable electronics., Comment: 18 pages