313 citations · 750 across the 46 of their papers we have counts for
52 papers
Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems
Xinyu Lin, Yashar Deldjoo, Sunhao Dai +7
The rapid integration of large language model-based agents into recommender systems has driven a shift from static, ranking-based pipelines toward autonomous and interactive system…
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…
Recommendation with Generative Models
Yashar Deldjoo, Zhankui He, Julian McAuley +8
Generative models are a class of AI models capable of creating new instances of data by learning and sampling from their statistical distributions. In recent years, these models ha…
Multi-modal Generative Models in Recommendation System
Arnau Ramisa, Rene Vidal, Yashar Deldjoo +8
Many recommendation systems limit user inputs to text strings or behavior signals such as clicks and purchases, and system outputs to a list of products sorted by relevance. With t…
Large Language Model Driven Recommendation
Anton Korikov, Scott Sanner, Yashar Deldjoo +8
While previous chapters focused on recommendation systems (RSs) based on standardized, non-verbal user feedback such as purchases, views, and clicks -- the advent of LLMs has unloc…
Understanding Modality Preferences in Search Clarification
Leila Tavakoli, Giovanni Castiglia, Federica Calo +3
This study is the first attempt to explore the impact of clarification question modality on user preference in search engines. We introduce the multi-modal search clarification dat…
XAI4LLM. Let Machine Learning Models and LLMs Collaborate for Enhanced In-Context Learning in Healthcare
Fatemeh Nazary, Yashar Deldjoo, Tommaso Di Noia +1
Clinical decision support systems require models that are not only highly accurate but also equitable and sensitive to the implications of missed diagnoses. In this study, we intro…
A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender System
Yashar Deldjoo, Fatemeh Nazary
The rapid adoption of large language models (LLMs) in recommender systems (RS) presents new challenges in understanding and evaluating their biases, which can result in unfairness…
Understanding Language Modeling Paradigm Adaptations in Recommender Systems: Lessons Learned and Open Challenges
Lemei Zhang, Peng Liu, Yashar Deldjoo +2
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 e…
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
Yashar Deldjoo, Zhankui He, Julian McAuley +7
Traditional recommender systems (RS) typically use user-item rating histories as their main data source. However, deep generative models now have the capability to model and sample…
CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System
Yashar Deldjoo, Tommaso di Noia
This work takes a critical stance on previous studies concerning fairness evaluation in Large Language Model (LLM)-based recommender systems, which have primarily assessed consumer…
A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems
Hossein A. Rahmani, Mohammadmehdi Naghiaei, Yashar Deldjoo
In recent years, there has been an increasing recognition that when machine learning (ML) algorithms are used to automate decisions, they may mistreat individuals or groups, with l…