ACM RecSys

The ACM Recommender Systems conference (RecSys) is the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems. Recommendation is a particular form of information filtering, that exploits past behaviors and user similarities to generate a list of information items that is personally tailored to an end-user’s preferences. As RecSys brings together the main international research groups working on recommender systems, along with many of the world’s leading e-commerce companies, it has become the most important annual conference for the presentation and discussion of recommender systems research.

Keynote Xavier Amatriain

Keynote Xavier Amatriain
NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation

NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
LLM-RecG: A Semantic Bias-Aware Framework for Zero-Shot Sequential Recommendation

LLM-RecG: A Semantic Bias-Aware Framework for Zero-Shot Sequential Recommendation
Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users

Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users
Lasso: Large Language Model-based User Simulator for Cross-Domain Recommendation

Lasso: Large Language Model-based User Simulator for Cross-Domain Recommendation
Hierarchical Graph Information Bottleneck for Multi-Behavior Recommendation

Hierarchical Graph Information Bottleneck for Multi-Behavior Recommendation
Enhancing Transferability and Consistency in Cross-Domain Recommendations via Supervised Disentangle

Enhancing Transferability and Consistency in Cross-Domain Recommendations via Supervised Disentangle
You Don’t Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control

You Don’t Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control
Mapping Stakeholder Needs to Multi-Sided Fairness in Candidate Recommendation for Algorithmic Hiring

Mapping Stakeholder Needs to Multi-Sided Fairness in Candidate Recommendation for Algorithmic Hiring
Integrating Individual and Group Fairness for Recommender Systems through Social Choice

Integrating Individual and Group Fairness for Recommender Systems through Social Choice
Emotion Vector-Based Fine-Tuning of Large Language Models for Age-Aware Teenage Book Recommendations

Emotion Vector-Based Fine-Tuning of Large Language Models for Age-Aware Teenage Book Recommendations
Cross-Batch Aggregation for Streaming Learning from Label Proportions in Industrial-Scale RecSys

Cross-Batch Aggregation for Streaming Learning from Label Proportions in Industrial-Scale RecSys
Breaking Knowledge Boundaries: Cognitive Distillation-enhanced Cross-Behavior Course Rec Model

Breaking Knowledge Boundaries: Cognitive Distillation-enhanced Cross-Behavior Course Rec Model
Affect aware Cross Domain Recommendation for Art Therapy via Music Preference Elicitation

Affect aware Cross Domain Recommendation for Art Therapy via Music Preference Elicitation
A Reproducibility Study of Product-side Fairness in Bundle Recommendation

A Reproducibility Study of Product-side Fairness in Bundle Recommendation
Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders

Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders
Recent Advances in Generative Conversational Recommender Systems

Recent Advances in Generative Conversational Recommender Systems
A Hands-on Dive Into Quantum Computing for Recommender Systems

A Hands-on Dive Into Quantum Computing for Recommender Systems
Keynote Jure Leskovec

Keynote Jure Leskovec
Suggest, Complement, Inspire: Story of Two-Tower Recommendations at Allegro.com

Suggest, Complement, Inspire: Story of Two-Tower Recommendations at Allegro.com
Scalable Data Debugging for Neighborhood-based Recommendation with Data Shapley Values

Scalable Data Debugging for Neighborhood-based Recommendation with Data Shapley Values
PinFM: Foundation Model for User Activity Sequences at a Billion-scale Visual Discovery Platform

PinFM: Foundation Model for User Activity Sequences at a Billion-scale Visual Discovery Platform
Test-Time Alignment with State Space Model for Tracking User Interest Shifts in Sequential Recsys

Test-Time Alignment with State Space Model for Tracking User Interest Shifts in Sequential Recsys
Not Just What, But When: Integrating Irregular Intervals to LLM for Sequential Recommendation

Not Just What, But When: Integrating Irregular Intervals to LLM for Sequential Recommendation
Let It Go? Not Quite: Addressing Item Cold Start in Sequential Recommendations with Content-Based

Let It Go? Not Quite: Addressing Item Cold Start in Sequential Recommendations with Content-Based
Agentic Personalisation of Cross-Channel Marketing Experiences

Agentic Personalisation of Cross-Channel Marketing Experiences
GRACE: Generative Recommendation via Journey-Aware Sparse Attention on Chain-of-Thought Tokenization

GRACE: Generative Recommendation via Journey-Aware Sparse Attention on Chain-of-Thought Tokenization
Personalized Interest Graphs for Theme-Driven User Behavior

Personalized Interest Graphs for Theme-Driven User Behavior
MoRE: A Mixture of Reflectors Framework for Large Language Model-Based Sequential Recommendation

MoRE: A Mixture of Reflectors Framework for Large Language Model-Based Sequential Recommendation
Track Highlights RecSys Challenge RecSys2025

Track Highlights RecSys Challenge RecSys2025