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

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, GENNEXT aims to advance discussions on the applications of language agents powered by Large Language Models (LLMs). The workshop will focus on enhancing interactivity between users and systems through multi-turn dialogues, improving creative content generation, advancing personalization, and enabling multifaceted, context-aware decision-making. For example, a language agent could respond to a query like ''Suggest an eco-friendly food tour for a weekend in my city'' by using a recommendation API to identify eateries specializing in sustainable or organic cuisine and a pollution API to ensure the selected routes have low air pollution levels.

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Published
2025-07-13
DOI
https://doi.org/10.1145/3726302.3730369
Primary Topic
Recommender Systems and Techniques
Type
article
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article

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

Yashar Deldjoo, Julian McAuley, Scott Sanner, Enrico Palumbo et al.
Recommender Systems and Techniques
article

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

Yashar Deldjoo, Julian McAuley, Scott Sanner, Enrico Palumbo, Pablo Castells, Hugues Bouchard, Shuai Zhang
article en

Abstract

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, GENNEXT aims to advance discussions on the applications of language agents powered by Large Language Models (LLMs). The workshop will focus on enhancing interactivity between users and systems through multi-turn dialogues, improving creative content generation, advancing personalization, and enabling multifaceted, context-aware decision-making. For example, a language agent could respond to a query like ''Suggest an eco-friendly food tour for a weekend in my city'' by using a recommendation API to identify eateries specializing in sustainable or organic cuisine and a pollution API to ensure the selected routes have low air pollution levels.

Amazon (United States) (US), University of Toronto (CA), UC San Diego Health System (US), Universidad Autónoma de Madrid (ES), Polytechnic University of Bari (IT)
Quality Education
Openalex Percentile: Top 75%
Recommender Systems and Techniques
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GENNEXT: The Next Generation of IR and Recommender Systems with Language Agents, Generative Models, and Conversational AI — Yashar Deldjoo, Julian McAuley, et al. · (2025) | TGRS Research Map | TGRS