Search Results for author:
Found 33 papers, 10 papers with code
XAI4LLM. Let Machine Learning Models and LLMs Collaborate for Enhanced In-Context Learning in Healthcare
This study outlines the development of a novel method for zero-shot/few-shot in-context learning (ICL) by integrating medical domain knowledge using a multi-layered structured prompt.
FairEvalLLM. A Comprehensive Framework for Benchmarking Fairness in Large Language Model Recommender Systems
This paper presents a framework for evaluating fairness in recommender systems powered by Large Language Models (RecLLMs), addressing the need for a unified approach that spans various fairness dimensions including sensitivity to user attributes, intrinsic fairness, and discussions of fairness based on underlying benefits.
Understanding Language Modeling Paradigm Adaptations in Recommender Systems: Lessons Learned and Open Challenges
The emergence of Large Language Models (LLMs) has achieved tremendous success in the field of Natural Language Processing owing to diverse training paradigms that empower LLMs to effectively capture intricate linguistic patterns and semantic representations.
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
Traditional recommender systems (RS) have used user-item rating histories as their primary data source, with collaborative filtering being one of the principal methods.
CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System
In the evolving landscape of recommender systems, the integration of Large Language Models (LLMs) such as ChatGPT marks a new era, introducing the concept of Recommendation via LLM (RecLLM).
A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems
Recommender systems are prominent examples of these machine learning (ML) systems that aid users in making decisions.
Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency
This study explores the nuanced capabilities and inherent biases of Recommender Systems using Large Language Models (RecLLMs), with a focus on ChatGPT-based systems.
Fairness for All: Investigating Harms to Within-Group Individuals in Producer Fairness Re-ranking Optimization -- A Reproducibility Study
This study reproduces previous PFR approaches and shows that they significantly harm colder items, leading to a fairness gap for these items in both advantaged and disadvantaged groups.
ChatGPT-HealthPrompt. Harnessing the Power of XAI in Prompt-Based Healthcare Decision Support using ChatGPT
In essence, this paper bridges the gap between AI and healthcare, proposing a novel methodology for LLMs application in clinical decision support systems.
Fairness of ChatGPT and the Role Of Explainable-Guided Prompts
Our research investigates the potential of Large-scale Language Models (LLMs), specifically OpenAI's GPT, in credit risk assessment-a binary classification task.
CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework
Point-of-Interest (POI ) recommendation systems have gained popularity for their unique ability to suggest geographical destinations with the incorporation of contextual information such as time, location, and user-item interaction.
Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids
In smart electrical grids, fault detection tasks may have a high impact on society due to their economic and critical implications.
Interactive Question Answering Systems: Literature Review
Interactive question answering is a recently proposed and increasingly popular solution that resides at the intersection of question answering and dialogue systems.
Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation
Recommending appropriate travel destinations to consumers based on contextual information such as their check-in time and location is a primary objective of Point-of-Interest (POI) recommender systems.
Fairness in Recommender Systems: Research Landscape and Future Directions
In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past.
CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems
Recently, there has been a rising awareness that when machine learning (ML) algorithms are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or economic consequences.
The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation
This paper studies the interplay between (i) the unfairness of active users, (ii) the unfairness of popular items, and (iii) the accuracy (personalization) of recommendation as three angles of our study triangle.
A Review of Modern Fashion Recommender Systems
The textile and apparel industries have grown tremendously over the last few years.
Simulations for novel problems in recommendation: analyzing misinformation and data characteristics
In this position paper, we discuss recent applications of simulation approaches for recommender systems tasks.
Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality
However, a key overlooked aspect has been the beyond-accuracy performance of APR, i. e., novelty, coverage, and amplification of popularity bias, considering that recent results suggest that BPR, the building block of APR, is sensitive to the intensification of biases and reduction of recommendation novelty.
Content-driven Music Recommendation: Evolution, State of the Art, and Challenges
Based on a thorough literature analysis, we first propose an onion model comprising five layers, each of which corresponds to a category of music content we identified: signal, embedded metadata, expert-generated content, user-generated content, and derivative content.
FedeRank: User Controlled Feedback with Federated Recommender Systems
Recommender systems have shown to be a successful representative of how data availability can ease our everyday digital life.
Multi-Step Adversarial Perturbations on Recommender Systems Embeddings
However, while the single-step fast gradient sign method (FGSM) is the most explored perturbation strategy, multi-step (iterative) perturbation strategies, that demonstrated higher efficacy in the computer vision domain, have been highly under-researched in recommendation tasks.
How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank
Recommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a vast space of possible choices.
Prioritized Multi-Criteria Federated Learning
In order to address these issues, Federated Learning (FL) has been recently proposed as a means to build ML models based on private datasets distributed over a large number of clients, while preventing data leakage.
A survey on Adversarial Recommender Systems: from Attack/Defense strategies to Generative Adversarial Networks
Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to their excellent performance and recommendation accuracy.
Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features
To raise awareness of this fact, we investigate differences between explicit user preferences and implicit user profiles.
Assessing the Impact of a User-Item Collaborative Attack on Class of Users
While previous works have focused on evaluating shilling attack strategies from a global perspective paying particular attention to the effect of the size of attacks and attacker's knowledge, in this work we explore the effectiveness of shilling attacks under novel aspects.
Towards Effective Device-Aware Federated Learning
With the wealth of information produced by social networks, smartphones, medical or financial applications, speculations have been raised about the sensitivity of such data in terms of users' personal privacy and data security.
Recommender Systems Fairness Evaluation via Generalized Cross Entropy
We present a probabilistic framework based on generalized cross entropy to evaluate fairness of recommender systems under this perspective, where we show that the proposed framework is flexible and explanatory by allowing to incorporate domain knowledge (through an ideal fair distribution) that can help to understand which item or user aspects a recommendation algorithm is over- or under-representing.
Session-Based Hotel Recommendations: Challenges and Future Directions
In the year 2019, the Recommender Systems Challenge deals with a real-world task from the area of e-tourism for the first time, namely the recommendation of hotels in booking sessions.
Using Mise-En-Scène Visual Features based on MPEG-7 and Deep Learning for Movie Recommendation
In this paper, we show that user's preferences on movies can be better described in terms of the mise-en-sc\`ene features, i. e., the visual aspects of a movie that characterize design, aesthetics and style (e. g., colors, textures).
Sparse vs. Non-sparse: Which One Is Better for Practical Visual Tracking?
Recently, sparse representation based visual tracking methods have attracted increasing attention in the computer vision community.
