313 citations · 750 across the 46 of their papers we have counts for
13 papers · 1 filter
Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation
Elena V. Epure, Yashar Deldjoo, Bruno Sguerra +2
Music Recommender Systems (MRSs) have long relied on an information retrieval framing, where progress is measured mainly through accuracy on retrieval-oriented subtasks. While effe…
ViLLA-MMBench: A Unified Benchmark Suite for LLM-Augmented Multimodal Movie Recommendation
Fatemeh Nazary, Ali Tourani, Yashar Deldjoo +1
Recommending long-form video content demands joint modeling of visual, audio, and textual modalities, yet most benchmarks address only raw features or narrow fusion. We present ViL…
Agentic Personalized Fashion Recommendation in the Age of Generative AI: Challenges, Opportunities, and Evaluation
Yashar Deldjoo, Nima Rafiee, Mahdyar Ravanbakhsh
Fashion recommender systems (FaRS) face distinct challenges due to rapid trend shifts, nuanced user preferences, intricate item-item compatibility, and the complex interplay among…
Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourism
Ashmi Banerjee, Adithi Satish, Fitri Nur Aisyah +2
We propose COLLAB-REC, a multi-agent framework designed to counteract popularity bias and improve diversity in tourism recommendations. In our setup, three LLM-based agents(Persona…
The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems
Reza Yousefi Maragheh, Yashar Deldjoo
Large language models (LLMs) are evolving from passive text generators into agentic systems that can plan, maintain state, invoke tools, and coordinate with other agents. This pers…
RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation
Ali Tourani, Fatemeh Nazary, Yashar Deldjoo
This paper addresses the challenge of building multimodal recommender systems for the movie domain, where sparse item metadata (e.g., title and genres) can limit retrieval quality…
Stealthy LLM-Driven Data Poisoning Attacks Against Embedding-Based Retrieval-Augmented Recommender Systems
Fatemeh Nazary, Yashar Deldjoo, Tommaso Di Noia +1
We present a systematic study of provider-side data poisoning in retrieval-augmented recommender systems (RAG-based). By modifying only a small fraction of tokens within item descr…
SynthTRIPs: A Knowledge-Grounded Framework for Benchmark Query Generation for Personalized Tourism Recommenders
Ashmi Banerjee, Adithi Satish, Fitri Nur Aisyah +2
Tourism Recommender Systems (TRS) are crucial in personalizing travel experiences by tailoring recommendations to users' preferences, constraints, and contextual factors. However,…
Toward Holistic Evaluation of Recommender Systems Powered by Generative Models
Yashar Deldjoo, Nikhil Mehta, Maheswaran Sathiamoorthy +3
Recommender systems powered by generative models (Gen-RecSys) extend beyond classical item ranking by producing open-ended content, which simultaneously unlocks richer user experie…
Use of Air Quality Sensor Network Data for Real-time Pollution-Aware POI Suggestion
Giuseppe Fasano, Yashar Deldjoo, Tommaso di Noia +6
This demo paper introduces AirSense-R, a privacy-preserving mobile application that delivers real-time, pollution-aware recommendations for urban points of interest (POIs). By merg…
Towards Recommender Systems LLMs Playground (RecSysLLMsP): Exploring Polarization and Engagement in Simulated Social Networks
Ljubisa Bojic, Zorica Dodevska, Yashar Deldjoo +1
Given the exponential advancement in AI technologies and the potential escalation of harmful effects from recommendation systems, it is crucial to simulate and evaluate these effec…
AirTOWN: A Privacy-Preserving Mobile App for Real-time Pollution-Aware POI Suggestion
Giuseppe Fasano, Yashar Deldjoo, Tommaso Di Noia
This demo paper presents \airtown, a privacy-preserving mobile application that provides real-time, pollution-aware recommendations for points of interest (POIs) in urban environme…
Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender Systems
Fatemeh Nazary, Yashar Deldjoo, Tommaso di Noia
This study presents Poison-RAG, a framework for adversarial data poisoning attacks targeting retrieval-augmented generation (RAG)-based recommender systems. Poison-RAG manipulates…