Yashar Deldjoo

Multimedia recommender systems

2018

This tutorial introduces multimedia recommender systems (MMRS), in particular, recommender systems that leverage multimedia content to recommend different media types. In contrast to the still most frequently adopted collaborative filtering approaches, we focus on content-based …

Assessing the Impact of a User-Item Collaborative Attack on Class of Users

2019 · arXiv (Cornell University)

Collaborative Filtering (CF) models lie at the core of most recommendation systems due to their state-of-the-art accuracy. They are commonly adopted in e-commerce and online services for their impact on sales volume and/or diversity, and …

How to put users in control of their data in federated top-N recommendation with learning to rank

2021

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. In such scenarios, data ownership …

Towards Multi-Modal Conversational Information Seeking

2021

Recent research on conversational information seeking (CIS) mostly focuses on uni-modal interactions and information items. This per- spective paper highlights the importance of moving towards de- veloping and evaluating multi-modal conversational information seeking (MMCIS) systems …

Interactive Question Answering Systems: Literature Review

2022 · arXiv (Cornell University)

Question answering systems are recognized as popular and frequently effective means of information seeking on the web. In such systems, information seekers can receive a concise response to their query by presenting their questions in …

Simulations for novel problems in recommendation: analyzing\n misinformation and data characteristics

2021 · arXiv (Cornell University)

In this position paper, we discuss recent applications of simulation\napproaches for recommender systems tasks. In particular, we describe how they\nwere used to analyze the problem of misinformation spreading and understand\nwhich data characteristics affect the performance …

CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System

2024 · arXiv (Cornell University)

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 fairness by comparing recommendation lists generated with and without sensitive user …

GENNEXT: The Next Generation of IR and Recommender Systems with Language Agents, Generative Models, and Conversational AI

2025

We present GENNEXT, a workshop dedicated to exploring the integration of language agents, generative models, and conversational AI within information retrieval (IR) and recommender systems (RS). Building on the success of our recent RecSys'24 workshop, …

RecSys Challenge 2017

2017

The ACM Recommender Systems Challenge 20171 focused on the problem of job recommendations: given a new job advertisement, the goal was to identify those users who are both (a) interested in getting notified about the …

Current challenges and visions in music recommender systems research

2018 · International Journal of Multimedia Information Retrieval

Music recommender systems (MRSs) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user’s fingertip. …

Movie genome: alleviating new item cold start in movie recommendation

2019 · User Modeling and User-Adapted Interaction

As of today, most movie recommendation services base their recommendations on collaborative filtering (CF) and/or content-based filtering (CBF) models that use metadata (e.g., genre or cast). In most video-on-demand and streaming services, however, new movies …

Recommender Systems Leveraging Multimedia Content

2020 · ACM Computing Surveys

Recommender systems have become a popular and effective means to manage the ever-increasing amount of multimedia content available today and to help users discover interesting new items. Today’s recommender systems suggest items of various media …