4 citations · 5 across the 11 of their papers we have counts for
11 papers
From "Strings" to "Things" for Personal Knowledge Graphs: Evaluating LLM Triple Extraction for Recommendation Systems
Abhirup Dasgupta, Fernando Spadea, Oshani Seneviratne
Personal Knowledge Graphs (PKGs) offer a privacy-preserving framework for modeling user preferences, yet constructing them from unstructured, decentralized conversational data rema…
From Risk to Rescue: An Agentic Survival Analysis Framework for Liquidation Prevention
Fernando Spadea, Oshani Seneviratne
Decentralized Finance (DeFi) lending protocols like Aave v3 rely on over-collateralization to secure loans, yet users frequently face liquidation due to volatile market conditions.…
Benchmarking Temporal Web3 Intelligence: Lessons from the FinSurvival 2025 Challenge
Oshani Seneviratne, Fernando Spadea, Adrien Pavao +2
Temporal Web analytics increasingly relies on large-scale, longitudinal data to understand how users, content, and systems evolve over time. A rapidly growing frontier is the \emph…
Federated Personal Knowledge Graph Completion with Lightweight Large Language Models for Personalized Recommendations
Fernando Spadea, Oshani Seneviratne
Personalized recommendation increasingly relies on private user data, motivating approaches that can adapt to individuals without centralizing their information. We present Federat…
Explainable Federated Learning for U.S. State-Level Financial Distress Modeling
Lorenzo Carta, Fernando Spadea, Oshani Seneviratne
We present the first application of federated learning (FL) to the U.S. National Financial Capability Study, introducing an interpretable framework for predicting consumer financia…
Aligning Language Models with Investor and Market Behavior for Financial Recommendations
Fernando Spadea, Oshani Seneviratne
Most financial recommendation systems often fail to account for key behavioral and regulatory factors, leading to advice that is misaligned with user preferences, difficult to inte…
Parallel and Multi-Stage Knowledge Graph Retrieval for Behaviorally Aligned Financial Asset Recommendations
Fernando Spadea, Oshani Seneviratne
Large language models (LLMs) show promise for personalized financial recommendations but are hampered by context limits, hallucinations, and a lack of behavioral grounding. Our pri…
Avoiding Over-Personalization with Rule-Guided Knowledge Graph Adaptation for LLM Recommendations
Fernando Spadea, Oshani Seneviratne
We present a lightweight neuro-symbolic framework to mitigate over-personalization in LLM-based recommender systems by adapting user-side Knowledge Graphs (KGs) at inference time.…
Federated Fine-Tuning of Large Language Models: Kahneman-Tversky vs. Direct Preference Optimization
Fernando Spadea, Oshani Seneviratne
We evaluate Kahneman-Tversky Optimization (KTO) as a fine-tuning method for large language models (LLMs) in federated learning (FL) settings, comparing it against Direct Preference…
Support and Scandals in GameFi dApps: A Network Analysis of The Sandbox Transactions
Fernando Spadea, Oshani Seneviratne
We explore the burgeoning field of GameFi through a detailed network analysis of The Sandbox, a prominent decentralized application (dApp) in this domain. Utilizing the bow-tie mod…
Assessing Scientific Contributions in Data Sharing Spaces
Kacy Adams, Fernando Spadea, Conor Flynn +1
In the present academic landscape, the process of collecting data is slow, and the lax infrastructures for data collaborations lead to significant delays in coming up with and diss…