GRAIL 2026 — Workshop on Generative, Retrieval-augmented, and Agentic Intelligence for Personalization. Co-located with CIKM 2026, Rome, Italy, November 8, 2026.

Paper submission deadline: August 24 August 28, 2026 (AoE)

About the Workshop

Overview

Personalization is being rebuilt around generative models. Large language models, retrieval-augmented generation, and agentic systems are transforming how recommender systems retrieve, rank, explain, converse with, and even simulate users — while surfacing hard new challenges around hallucination, controllability, evaluation, privacy, fairness, and long-term user impact.

GRAIL 2026 is a workshop at CIKM 2026 dedicated to generative recommender systems: methods that use generative models for retrieval, ranking, planning, and explanation, as well as for conversational agents, user simulation, and evaluation. It brings academic researchers and industry practitioners together to push these approaches forward while giving equal attention to their reliability, safety, and responsible deployment.

Whether you build, study, or deploy recommender systems, we invite you to take part. See the Call for Papers for topics and submission details, and join us in Rome on November 8, 2026.

Call for Papers

We invite contributions

GRAIL 2026 welcomes original research, position pieces, and practical contributions at the intersection of generative AI and personalization. Topics include, but are not limited to:

LLM-based recommendation Retrieval-augmented personalization Generative sequential & session-based recommendation Multimodal & content-generating recommenders Conversational & agentic recommendation Knowledge-grounded recommendation Synthetic interaction data User modeling with GenAI Evaluation of generated recommendations Fairness, privacy & safety Controllability & user feedback Scalable production architectures

Full papers

max 8 pages

Mature research results. Maximum 8 pages, plus unlimited pages for references, appendix, and GenAI-usage disclosure.

Short papers

max 4 pages

Ongoing work, demos, position, or opinion papers. Maximum 4 pages, plus unlimited pages for references, appendix, and GenAI-usage disclosure.

All submissions follow the standard ACM double-column template. Each paper receives at least three reviews. Papers are non-archival, so authors remain free to submit revised versions elsewhere. At least one author of each accepted paper must register and present in person in Rome. Submissions are handled via EasyChair.

Formatting & templates

Following the ACM publication workflow, all manuscripts must be submitted for review in the standard ACM double-column format. Use the latest official ACM templates:

The recommended setting for LaTeX documents is: \documentclass[sigconf, review]{acmart}

Review process: GRAIL 2026 uses single-blind review. Authors should include their names and affiliations in the submission — anonymization is not required.

Mark your calendar

Important Dates

August 24, 2026August 28, 2026
Paper submission deadline
September 23, 2026
Acceptance notification
October 5, 2026
Camera-ready deadline
November 8, 2026
Workshop day (Rome)

All deadlines are 11:59 PM Anywhere on Earth (AoE).

The Team

Organizers

GRAIL 2026 is organized by a team spanning academia and industry.

Invited Talks

Keynotes

Keynote 1
Sudeep Das — Head of AI/ML, New Verticals, DoorDash

Sudeep Das

Head of AI/ML, New Verticals — DoorDash

Keynote 2
Fabrizio Silvestri — Full Professor, Sapienza University of Rome

Fabrizio Silvestri

Full Professor, Department of Computer, Control and Management Engineering, Sapienza University of Rome

Keynote 3
Andreas Damianou — Senior Research Manager, Spotify

Andreas Damianou

Senior Research Manager — Spotify

Schedule

Program & Accepted Papers

Sunday, November 8, 2026 · 09:00–13:00 · Half-day, in-person

Venue: San Pietro in Vincoli Campus, Sapienza University of Rome — Faculty of Engineering, Via Eudossiana 18, 00184 Roma  (venue details & directions)

09:00–09:10
Opening remarks & workshop goals
09:10–09:35
Keynote 1 — Sudeep Das, DoorDash20 min talk + 5 min Q&A
09:35–10:20
Paper Session 1 — Long Papers12 min presentation + 3 min Q&A each
09:35–09:50
When Do Graphs Beat Text? Knowledge-Graph Embeddings as a Content-Free Candidate-Generation Substrate for RecommendationLong
Karima Boutalbi, Rim Hantach, Olivier Le Van, Rafika Boutalbi
PDF
09:50–10:05
A Technique to Model Product Information and User Reviews (MPIUR)Long
Emna Belguith, Fajrian Yunus, Talel Abdessalem
PDF
10:05–10:20
SIFT: Search Intent-to-Filter Transformer for Multi-Task Personalized Filter Ranking at AirbnbLong
Shashank Dabriwal, Tanya Piplani, Hao Li, Yiwei Wang, Ashish Jain, Kedar Bellare, Stephanie Moyerman
PDF
10:20–10:40
Paper Session 2 — Short Papers8 min presentation + 2 min Q&A each
10:20–10:30
SCOUT: Supply-Aware Cold-Start Proactive Query Suggestion for Travel SearchShort
Hao Li, Shashank Reddy, Kedar Bellare, Ashish Jain, Stephanie Moyerman
PDF
10:30–10:40
PALATE: Pretrained Autoregressive LATent EmbeddingsShort
Sizhang Zhao, Praveen Gondhi, Bin Li, Utsaw Kumar
PDF
10:40–10:45
Buffer / transition
10:45–11:30
☕ Coffee break
11:30–11:55
Keynote 2 — Fabrizio Silvestri, Sapienza University of Rome20 min talk + 5 min Q&A
11:55–12:30
Paper Session 3 — Long & Short Papers
11:55–12:10
OptiCover: A Scalable and Multi-Surface Framework for Cold-Start Visual Optimization under Semantic ConstraintsLong
Guillaume Guy, Mihajlo Grbovic, Joe Mills, Selen Uguroglu, Liwei He, Pratiksha Kadam, Nalini Kartha, Qian Aria Li
PDF
12:10–12:20
From Head to Tail: LLM-Augmented Sequential Recommendation and Trade-off OptimizationShort
Adeep Hande, Shaghayegh Agah, Neeraj Sharma, Karina Halevy, Sardar Hamidian
PDF
12:20–12:30
Critiquing Helps, Robustly: A Stress-Tested Agentic RAG Recommender with LLM User SimulationShort
Menon Bharath Sreekumar
PDF
12:30–12:55
Keynote 3 — Andreas Damianou, Spotify20 min talk + 5 min Q&A
12:55–13:00
Closing remarks
Get in touch

Contact

For questions about the workshop, please contact the organizing team at grail2026-cikm@googlegroups.com, or reach out to Ashish Jain directly at ashish.jain@airbnb.com.