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Found 11 papers, 2 papers with code
Stock Recommendations for Individual Investors: A Temporal Graph Network Approach with Diversification-Enhancing Contrastive Learning
Hence, the tricky point in stock recommendation is that recommendations should give good investment performance but also should not ignore individual preferences.
Temporal Graph Networks for Graph Anomaly Detection in Financial Networks
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.
A Recommender System for NFT Collectibles with Item Feature
Recommender systems have been actively studied and applied in various domains to deal with information overload.
A Temporal Graph Network Framework for Dynamic Recommendation
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.
Ignore Me But Don't Replace Me: Utilizing Non-Linguistic Elements for Pretraining on the Cybersecurity Domain
We use our domain-customized methodology to train CyBERTuned, a cybersecurity domain language model that outperforms other cybersecurity PLMs on most tasks.
NFTs to MARS: Multi-Attention Recommender System for NFTs
Recommender systems have become essential tools for enhancing user experiences across various domains.
Mean-Variance Efficient Collaborative Filtering for Stock Recommendation
In this regard, we propose a mean-variance efficient collaborative filtering (MVECF) model for stock recommendations that consider both aspects.
DarkBERT: A Language Model for the Dark Side of the Internet
Recent research has suggested that there are clear differences in the language used in the Dark Web compared to that of the Surface Web.
MF-NeRF: Memory Efficient NeRF with Mixed-Feature Hash Table
Neural radiance field (NeRF) has shown remarkable performance in generating photo-realistic novel views.
Shedding New Light on the Language of the Dark Web
By leveraging CoDA, we conduct a thorough linguistic analysis of the Dark Web and examine the textual differences between the Dark Web and the Surface Web.
DEEP-BO for Hyperparameter Optimization of Deep Networks
The performance of deep neural networks (DNN) is very sensitive to the particular choice of hyper-parameters.
